Triple
T27931537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badlapur |
E707993
|
entity |
| Predicate | distanceFromMumbaiApprox |
P74678
|
FINISHED |
| Object | about 60 km by rail |
—
|
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 60 km by rail | Statement: [Badlapur, distanceFromMumbaiApprox, about 60 km by rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMumbaiApprox Context triple: [Badlapur, distanceFromMumbaiApprox, about 60 km by rail]
-
A.
distanceFromMumbaiApproxKm
chosen
Indicates the approximate physical distance, measured in kilometers, between a given location and Mumbai.
-
B.
distanceFromPune
Indicates the spatial distance between a given location or entity and the city of Pune.
-
C.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
-
D.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
E.
distanceToDelhiApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Delhi.
- 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_69ef96bbf2c48190a9d0e0291457aab6 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69ff9e91bba08190af04b31ad815b13a |
completed | May 9, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69ff9e00e4808190bde8f07e6519a72c |
completed | May 9, 2026, 8:50 p.m. |
Created at: April 27, 2026, 7:03 p.m.