Triple
T7957318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agartala |
E184771
|
entity |
| Predicate | distanceToDhakaByRoadApproxKm |
P80017
|
FINISHED |
| Object | 150 |
—
|
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: 150 | Statement: [Agartala, distanceToDhakaByRoadApproxKm, 150]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDhakaByRoadApproxKm Context triple: [Agartala, distanceToDhakaByRoadApproxKm, 150]
-
A.
distanceToBangladeshBorder_km
Indicates the distance, measured in kilometers, from a given location to the nearest point on the border of Bangladesh.
-
B.
distanceFromGuwahati_km
Indicates the physical distance, measured in kilometers, between an entity and the location of Guwahati.
-
C.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
-
D.
distanceToSiliguri_km
Indicates the physical distance, measured in kilometers, between a given location and Siliguri.
-
E.
distanceToDelhiByRoad_km
Indicates the road travel distance, measured in kilometers, from a given place to Delhi.
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b7ebb24819094bc011d51ef63fb |
completed | March 31, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69cb0473d7dc8190a25d0cf460b9fcbe |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14bbbacc81909c6cf8ec35314bbb |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:11 p.m.