Triple
T1127798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Newark, New Jersey |
E24758
|
entity |
| Predicate | distanceToNewYorkCityKm |
P2383
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [Newark, New Jersey, distanceToNewYorkCityKm, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNewYorkCityKm Context triple: [Newark, New Jersey, distanceToNewYorkCityKm, 13]
-
A.
distanceToNewYorkCity
chosen
Indicates the spatial distance between a given entity’s location and New York City.
-
B.
distanceToIstanbulApproxKm
Indicates the approximate distance, measured in kilometers, between a given place and Istanbul.
-
C.
approximateDistanceKm
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
D.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
-
E.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bc4bc21881909dcfe628f59f3e8c |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb48de2081909a0dce005b1c9df1 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.