Triple
T16153245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milltown, New Jersey |
E391965
|
entity |
| Predicate | proximityToEmploymentCenters |
P71206
|
FINISHED |
| Object | New Brunswick employment centers |
—
|
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: New Brunswick employment centers | Statement: [Milltown, New Jersey, proximityToEmploymentCenters, New Brunswick employment centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToEmploymentCenters Context triple: [Milltown, New Jersey, proximityToEmploymentCenters, New Brunswick employment centers]
-
A.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
campusProximity
Indicates that one entity is located near, adjacent to, or within a short distance of a campus associated with the other entity.
-
C.
distanceToWokingTownCentre
Indicates the spatial distance between a given location and the center of Woking town.
-
D.
nearbyEconomicActivity
chosen
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
-
E.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
- 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d98d08c8190a15d4aee40d47220 |
completed | April 17, 2026, 11:46 a.m. |
| PD | Predicate disambiguation | batch_69e1828abb608190a99d86bce1d77de2 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 5:01 a.m.