Triple
T29506126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dadri |
E748516
|
entity |
| Predicate | nearestMetroCity |
P68234
|
FINISHED |
| Object | Delhi |
—
|
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: Delhi | Statement: [Dadri, nearestMetroCity, Delhi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearestMetroCity Context triple: [Dadri, nearestMetroCity, Delhi]
-
A.
nearestMajorMetro
chosen
Indicates the relationship where a given location is associated with the closest large metropolitan area to it.
-
B.
nearestCityTo
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
-
C.
nearestMajorCity
Indicates that one city is the closest significant urban center to another location or city compared to all other major cities.
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
E.
largestNearbyCity
Indicates that one city is the largest (by population, area, or another defined metric) among the cities located within a specified nearby region of another place or city.
- 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_69f0bd455a9c8190b40a3e8ea38cf61f |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
Created at: April 28, 2026, 4:28 p.m.