Triple
T98766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orteig Prize |
E1992
|
entity |
| Predicate | sponsorOccupation |
P3548
|
FINISHED |
| Object | hotelier |
—
|
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: hotelier | Statement: [Orteig Prize, sponsorOccupation, hotelier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sponsorOccupation Context triple: [Orteig Prize, sponsorOccupation, hotelier]
-
A.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
victimOccupation
Indicates the profession or job role held by the person who is the victim in an event or incident.
-
C.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
D.
sponsorType
Indicates the specific role or category of sponsorship that an entity provides in relation to another entity or event.
-
E.
authorOccupation
Indicates the professional role or job that an author holds or is associated with.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24ff07d148190a59aee12c807659d |
completed | Feb. 28, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69a24ebe7b1c8190a6bfbf31dc7c7f07 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a24f4b4658819087902414959161fb |
completed | Feb. 28, 2026, 2:13 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.