Triple
T2778648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regius Professor of Modern History at the University of Cambridge |
E61637
|
entity |
| Predicate | isRoyalProfessorship |
P17683
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 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: true | Statement: [Regius Professor of Modern History at the University of Cambridge, isRoyalProfessorship, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isRoyalProfessorship Context triple: [Regius Professor of Modern History at the University of Cambridge, isRoyalProfessorship, true]
-
A.
hasHonoraryDegreeFrom
Indicates that an individual has been awarded an honorary degree by a particular institution.
-
B.
notableProfessor
Indicates that a person holds or has held a professorship that is distinguished, prominent, or otherwise recognized as notable.
-
C.
hasAcademicRank
Indicates that an entity holds a specific academic rank or title within an educational or research institution.
-
D.
isRoyalTitle
chosen
Indicates that something is a formal title associated with royalty or a royal rank.
-
E.
hasDoctoralDegreeFrom
Indicates that an individual holds a doctoral-level academic degree that was awarded by a specified institution.
- F. None of above.
Provenance (3 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_69ab4b7e43c48190997b8fc8fb1663ab |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abddceb9d88190961e30d521a21552 |
completed | March 7, 2026, 8:11 a.m. |
| PD | Predicate disambiguation | batch_69abdd00b65c8190a8ea444308c4fa2b |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:57 p.m.