Triple
T7795758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kodaikanal |
E180293
|
entity |
| Predicate | distanceFromMadurai |
P79070
|
FINISHED |
| Object | about 120 kilometres by road |
—
|
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 120 kilometres by road | Statement: [Kodaikanal, distanceFromMadurai, about 120 kilometres by road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMadurai Context triple: [Kodaikanal, distanceFromMadurai, about 120 kilometres by road]
-
A.
distanceFromMathura
Indicates the spatial distance between a given entity or location and the city of Mathura.
-
B.
distanceFromChennai
Indicates the spatial distance between a given entity or location and the city of Chennai.
-
C.
distanceFromTirupati
Indicates the measured distance between a given location and Tirupati.
-
D.
distanceFromChandigarh_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Chandigarh.
-
E.
distanceFromJaipur
Indicates the spatial distance between a given entity and the location Jaipur.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78a6d88819093f83528fe88b182 |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae9111b2481909684a2d4aa4831c2 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:31 p.m.