Triple
T6928179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indira Point |
E160363
|
entity |
| Predicate | distanceToChennai |
P37294
|
FINISHED |
| Object | about 1250 km (approximate) |
—
|
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: about 1250 km (approximate) | Statement: [Indira Point, distanceToChennai, about 1250 km (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToChennai Context triple: [Indira Point, distanceToChennai, about 1250 km (approximate)]
-
A.
distanceFromChennai
chosen
Indicates the spatial distance between a given entity or location and the city of Chennai.
-
B.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
C.
distanceFromBangalore
Indicates the spatial distance separating a given entity or location from Bangalore.
-
D.
distanceToHyderabad
Indicates the spatial distance between a given entity’s location and the city of Hyderabad.
-
E.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
- 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_69c6884d350081908d8a970e4d40ad78 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da1de28881908579bc198e74203e |
completed | March 27, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69c6d7bb577c81908ee8b415b4281f3d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:27 p.m.