Triple
T7868726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Department of Physics, King's College London |
E182682
|
entity |
| Predicate | affiliation |
P10
|
FINISHED |
| Object |
King's College London Faculty of Natural, Mathematical & Engineering Sciences
King's College London Faculty of Natural, Mathematical & Engineering Sciences is an academic division of King's College London that encompasses teaching and research in disciplines such as physics, mathematics, computer science, engineering, and related natural sciences.
|
E34099
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: King's College London Faculty of Natural, Mathematical & Engineering Sciences | Statement: [Department of Physics, King's College London, affiliation, King's College London Faculty of Natural, Mathematical & Engineering Sciences]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: King's College London Faculty of Natural, Mathematical & Engineering Sciences Context triple: [Department of Physics, King's College London, affiliation, King's College London Faculty of Natural, Mathematical & Engineering Sciences]
-
A.
King’s College London
King’s College London is a major public research university in London, England, renowned for its contributions to fields such as medicine, law, and the humanities.
-
B.
University College London
University College London is a major public research university in London renowned for its multidisciplinary teaching, pioneering research, and global academic influence.
-
C.
Imperial College London
Imperial College London is a leading public research university in London renowned for its strengths in science, engineering, medicine, and business.
-
D.
University of Queen Mary London
The University of Queen Mary London is a major public research university in London renowned for its strong academic programs and membership in the prestigious Russell Group of leading UK universities.
-
E.
University of Oxford Faculty of Mathematical, Physical and Life Sciences
The University of Oxford Faculty of Mathematical, Physical and Life Sciences is a major academic division of the University of Oxford that oversees teaching and research in the mathematical, physical, engineering, and life sciences.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: King's College London Faculty of Natural, Mathematical & Engineering Sciences Triple: [Department of Physics, King's College London, affiliation, King's College London Faculty of Natural, Mathematical & Engineering Sciences]
Generated description
King's College London Faculty of Natural, Mathematical & Engineering Sciences is an academic division of King's College London that encompasses teaching and research in disciplines such as physics, mathematics, computer science, engineering, and related natural sciences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: King's College London Faculty of Natural, Mathematical & Engineering Sciences Target entity description: King's College London Faculty of Natural, Mathematical & Engineering Sciences is an academic division of King's College London that encompasses teaching and research in disciplines such as physics, mathematics, computer science, engineering, and related natural sciences.
-
A.
King’s College London
chosen
King’s College London is a major public research university in London, England, renowned for its contributions to fields such as medicine, law, and the humanities.
-
B.
University College London
University College London is a major public research university in London renowned for its multidisciplinary teaching, pioneering research, and global academic influence.
-
C.
Imperial College London
Imperial College London is a leading public research university in London renowned for its strengths in science, engineering, medicine, and business.
-
D.
University of Queen Mary London
The University of Queen Mary London is a major public research university in London renowned for its strong academic programs and membership in the prestigious Russell Group of leading UK universities.
-
E.
University of Oxford Faculty of Mathematical, Physical and Life Sciences
The University of Oxford Faculty of Mathematical, Physical and Life Sciences is a major academic division of the University of Oxford that oversees teaching and research in the mathematical, physical, engineering, and life sciences.
- F. None of above.
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3848d6d88190830afcf04ad12154 |
completed | March 31, 2026, 2:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b60b3b08190832837bacb8ce965 |
completed | March 31, 2026, 5:28 a.m. |
| NEDg | Description generation | batch_69cb7630b8908190a0b8f4856bceea0a |
completed | March 31, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cbbfb894588190971ade076acdbd5c |
completed | March 31, 2026, 12:36 p.m. |
Created at: March 30, 2026, 4:55 p.m.