Triple
T26592958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baraut |
E667409
|
entity |
| Predicate | distanceToMeerut |
P197157
|
FINISHED |
| Object | approximately 50–60 kilometres |
—
|
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 50–60 kilometres | Statement: [Baraut, distanceToMeerut, approximately 50–60 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMeerut Context triple: [Baraut, distanceToMeerut, approximately 50–60 kilometres]
-
A.
distanceFromGhaziabad
Indicates the measured or specified distance separating a given entity or location from Ghaziabad.
-
B.
distanceToDelhiApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Delhi.
-
C.
distanceFromGurugram_km
Indicates the physical distance, measured in kilometers, between an entity’s location and Gurugram.
-
D.
distanceToLucknow
Indicates the spatial distance between a given entity’s location and the city of Lucknow.
-
E.
distanceFromPatna
Indicates the spatial distance between a given location and the city of Patna.
- 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_69ee9cfc385081909ac9ae178030a06e |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69fe7b1b3460819081172731b52ac15b |
completed | May 9, 2026, 12:08 a.m. |
Created at: April 27, 2026, 2:09 a.m.