Triple
T2882702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rudy Giuliani |
E59433
|
entity |
| Predicate | officeStartForMayorOfNewYorkCity |
P42554
|
FINISHED |
| Object | 1994-01-01 |
—
|
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: 1994-01-01 | Statement: [Rudy Giuliani, officeStartForMayorOfNewYorkCity, 1994-01-01]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeStartForMayorOfNewYorkCity Context triple: [Rudy Giuliani, officeStartForMayorOfNewYorkCity, 1994-01-01]
-
A.
officeEndForMayorOfToronto
Indicates the date or event marking when an individual's term as Mayor of Toronto comes to an end.
-
B.
officeStartForPresident
Indicates the date or point in time when a person begins serving in the role of president.
-
C.
hasBoroughPresident
Indicates that a borough is associated with a specific individual who serves as its president or chief elected official.
-
D.
hasCityManager
Indicates that an entity has a specific individual who serves in the role of city manager for it.
-
E.
wonMayoraltyOf
Indicates that one entity won the election to become mayor of a specified place or jurisdiction.
- 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe02c238881908f7a349563c388bf |
completed | March 7, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69abdd15cbf08190bf7fea5ea516848a |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdd96670c8190b727f9ac27dadf67 |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 10:03 p.m.