Triple
T30271320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William A. Schnader Professor of Law |
E769805
|
entity |
| Predicate | benefitToHolder |
P2188
|
FINISHED |
| Object | prestige |
—
|
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: prestige | Statement: [William A. Schnader Professor of Law, benefitToHolder, prestige]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitToHolder Context triple: [William A. Schnader Professor of Law, benefitToHolder, prestige]
-
A.
beneficiaryRight
Indicates that one entity holds a right or entitlement to receive benefits, advantages, or proceeds arising from another entity or arrangement.
-
B.
beneficiaryProvision
Indicates that one entity provides something (such as goods, services, or benefits) for the advantage or benefit of another entity.
-
C.
beneficiaryContribution
Indicates that one party provides a contribution, support, or resources that benefit another designated beneficiary.
-
D.
hasBenefit
chosen
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
- 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_69f224856d9881908c7f0dd64f059672 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 29, 2026, 7:43 p.m.