Triple
T37988416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chiplun |
E947758
|
entity |
| Predicate | distanceToRatnagiri_km |
P204517
|
FINISHED |
| Object | approximately 90 |
—
|
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 | Statement: [Chiplun, distanceToRatnagiri_km, approximately 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToRatnagiri_km Context triple: [Chiplun, distanceToRatnagiri_km, approximately 90]
-
A.
distanceFromKangra_km
Indicates the physical distance, measured in kilometers, between an entity and the location Kangra.
-
B.
distanceToSiliguri_km
Indicates the physical distance, measured in kilometers, between a given location and Siliguri.
-
C.
distanceToSrinagar_km
Indicates the physical distance, measured in kilometers, between a given place or entity and the city of Srinagar.
-
D.
distanceToGondia_km
Indicates the physical distance, measured in kilometers, between a given location and Gondia.
-
E.
distanceFromRishikesh_km
Indicates the physical distance, measured in kilometers, between a given place and Rishikesh.
- 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_69f76ef8a1d08190a741bbbc5970e3b3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:20 p.m.