Triple
T27741809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Jersey politics |
E701873
|
entity |
| Predicate | majorUrbanPoliticalCenters |
P43828
|
FINISHED |
| Object | Newark |
—
|
NE NERFINISHED |
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: Newark | Statement: [New Jersey politics, majorUrbanPoliticalCenters, Newark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorUrbanPoliticalCenters Context triple: [New Jersey politics, majorUrbanPoliticalCenters, Newark]
-
A.
majorUrbanCenterIn
Indicates that a city or metropolitan area functions as a primary or significant urban center within the specified region or administrative unit.
-
B.
capitalOrMajorCenter
chosen
Indicates that a location serves as a capital city or a primary major center (political, economic, or cultural hub) for a larger region or entity.
-
C.
majorPopulationCenter
Indicates that a location functions as a primary hub of population concentration and activity within a region.
-
D.
developedUrbanCenters
Indicates that an entity has created, expanded, or significantly contributed to the growth of urban population centers or cities.
-
E.
coversUrbanAreas
Indicates that something extends over, includes, or provides coverage for urban or metropolitan areas.
- 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_69ef6a53c7388190899baa6daf42301c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fccdd496048190bca801a8a9eecb62 |
completed | May 7, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
Created at: April 27, 2026, 4:11 p.m.