Triple
T13228737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kishanganj |
E314951
|
entity |
| Predicate | distanceToBagdograAirport |
P108629
|
FINISHED |
| Object | approximately 90 km |
—
|
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: approximately 90 km | Statement: [Kishanganj, distanceToBagdograAirport, approximately 90 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBagdograAirport Context triple: [Kishanganj, distanceToBagdograAirport, approximately 90 km]
-
A.
distanceFromGuwahati_km
Indicates the physical distance, measured in kilometers, between an entity and the location of Guwahati.
-
B.
distanceFromAgartala
Indicates the measured spatial distance between a given entity and the location of Agartala.
-
C.
distanceToPantnagarAirport
Indicates the measured distance between a given location or entity and Pantnagar Airport.
-
D.
distanceToSiliguri_km
Indicates the physical distance, measured in kilometers, between a given location and Siliguri.
-
E.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d3232d48190a3c792b025c596a6 |
completed | April 10, 2026, 11:52 p.m. |
| PD | Predicate disambiguation | batch_69d98bcb21648190aef241de1e7887e2 |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98c959ba08190adf29dc0c4e1fca6 |
completed | April 10, 2026, 11:49 p.m. |
Created at: April 9, 2026, 9:21 p.m.