Triple
T37988415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chiplun |
E947758
|
entity |
| Predicate | distanceToMumbai_km |
P74678
|
FINISHED |
| Object | approximately 250 |
—
|
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 250 | Statement: [Chiplun, distanceToMumbai_km, approximately 250]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMumbai_km Context triple: [Chiplun, distanceToMumbai_km, approximately 250]
-
A.
distanceFromMumbaiApproxKm
chosen
Indicates the approximate physical distance, measured in kilometers, between a given location and Mumbai.
-
B.
distanceToDelhiApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Delhi.
-
C.
distanceToDelhiByRoad_km
Indicates the road travel distance, measured in kilometers, from a given place to Delhi.
-
D.
distanceToDelhiByRail_km
Indicates the distance, measured in kilometers, from a given place to Delhi when traveling by rail.
-
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_69f76ef8a1d08190a741bbbc5970e3b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:20 p.m.