Triple
T25897131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Pataki |
E652497
|
entity |
| Predicate | numberOfTermsInOfficeAsGovernorOfNewYork |
P17900
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [George Pataki, numberOfTermsInOfficeAsGovernorOfNewYork, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTermsInOfficeAsGovernorOfNewYork Context triple: [George Pataki, numberOfTermsInOfficeAsGovernorOfNewYork, 3]
-
A.
succeededBy (Governor of New York)
Indicates that one individual directly follows another in holding the office of Governor of New York.
-
B.
governorTerm
Indicates the time period during which a person holds or held the office of governor of a specific jurisdiction.
-
C.
numberOfTermsAsGovernor
chosen
Indicates the number of separate terms an individual has served in the role of governor.
-
D.
numberOfTermsAsMayor
Indicates the number of distinct terms an individual has served in the role of mayor.
-
E.
servedAsGovernorUntil
Indicates that an entity held the position of governor up to a specified end date or time.
- 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_69e7ab3c6cc081908de59bfcc28ec19d |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fd0d0ba5c48190bddb3f0e6637544c |
completed | May 7, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69fd0c4324a8819086c90adf46216e0e |
completed | May 7, 2026, 10:03 p.m. |
Created at: April 22, 2026, 8:23 a.m.