Triple
T15526735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reading Power Station |
E369102
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Reading C
Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
|
E1161526
|
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: Reading C | Statement: [Reading Power Station, hasComponent, Reading C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reading C Context triple: [Reading Power Station, hasComponent, Reading C]
-
A.
On to C (programming book)
"On to C" is a programming book by Patrick Henry Winston that introduces and teaches the C language with an emphasis on clear explanations and practical examples for learners.
-
B.
C
C is a tram route designation used in the Strasbourg tramway network in France.
-
C.
C
C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
-
D.
C
C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
-
E.
C
C is one of the three central women in Edward Albee’s play "Three Tall Women," representing a younger stage of the protagonist’s life and perspective.
- 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: Reading C Triple: [Reading Power Station, hasComponent, Reading C]
Generated description
Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Reading C Target entity description: Reading C is a generating unit within the Reading Power Station complex, contributing to the facility’s overall electricity production.
-
A.
On to C (programming book)
"On to C" is a programming book by Patrick Henry Winston that introduces and teaches the C language with an emphasis on clear explanations and practical examples for learners.
-
B.
C
C is a foundational, general-purpose programming language known for its efficiency, low-level memory access, and influence on many later languages such as C++, Java, and Python.
-
C.
C
C is a light rail service designation used by the Los Angeles Metro system for one of its primary rail lines.
-
D.
C
C is the New York Stock Exchange ticker symbol for Citigroup Inc., a major global financial services and banking corporation.
-
E.
C
C is one of the three central women in Edward Albee’s play "Three Tall Women," representing a younger stage of the protagonist’s life and perspective.
- F. None of above. chosen
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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e04145178481909fb0339a79d4239e |
completed | April 16, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d598e6c8190870e9249197f5f53 |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3de663848190936a5b1d31d18c75 |
completed | May 9, 2026, 2 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3e8c5f308190a335dbc45f9d88ee |
completed | May 9, 2026, 2:02 p.m. |
Created at: April 10, 2026, 4:05 a.m.