Triple
T2536286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manhattan Borough President |
E56275
|
entity |
| Predicate | numberOfBoroughPresidentsInCity |
P40959
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Manhattan Borough President, numberOfBoroughPresidentsInCity, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBoroughPresidentsInCity Context triple: [Manhattan Borough President, numberOfBoroughPresidentsInCity, 5]
-
A.
hasBoroughPresident
Indicates that a borough is associated with a specific individual who serves as its president or chief elected official.
-
B.
numberOfBoroughs
Indicates the total count of boroughs associated with a given entity.
-
C.
numberOfTermsAsMayor
Indicates the number of distinct terms an individual has served in the role of mayor.
-
D.
isBoroughSeatOf
Indicates that a place serves as the administrative center or seat of government for a specific borough.
-
E.
hasMayor
Indicates that one entity serves as the mayor of another entity, typically a city, town, or municipality.
- 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd648487881908ce8ca22def77294 |
completed | March 7, 2026, 7:39 a.m. |
Created at: March 6, 2026, 9:47 p.m.