Triple
T18626114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clifton R. Musser Professor of Economics |
E455287
|
entity |
| Predicate | namedProfessorship |
P132444
|
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: [Clifton R. Musser Professor of Economics, namedProfessorship, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedProfessorship Context triple: [Clifton R. Musser Professor of Economics, namedProfessorship, true]
-
A.
notableProfessor
Indicates that a person holds or has held a professorship that is distinguished, prominent, or otherwise recognized as notable.
-
B.
isProfessorshipIn
Indicates that a professorship position is associated with or belongs to a specific academic field, department, or institution.
-
C.
academicChair
Indicates that one entity serves as the academic chair (leader or head) of a department, program, or academic unit associated with the other entity.
-
D.
namedAfterInstitution
Indicates that one entity has been given a name derived from or in honor of an institution.
-
E.
honorificDegree
Indicates that an entity has been awarded an honorary academic degree or title, typically in recognition of merit rather than completion of formal study.
- F. None of above. chosen
Provenance (4 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54f04d0cc81909c3c6022c5dfdb63 |
completed | April 19, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69e478d4a7948190a4bb9223bb5dddfc |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e485f5d1588190b44f31cbc54c0a9d |
completed | April 19, 2026, 7:36 a.m. |
Created at: April 10, 2026, 11:46 a.m.