Triple
T13712417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babatpur |
E328805
|
entity |
| Predicate | distanceToVaranasiCityCentre_km |
P28434
|
FINISHED |
| Object | approximately 20 |
—
|
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 20 | Statement: [Babatpur, distanceToVaranasiCityCentre_km, approximately 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToVaranasiCityCentre_km Context triple: [Babatpur, distanceToVaranasiCityCentre_km, approximately 20]
-
A.
distanceFromVaranasi
chosen
Indicates the measured or specified distance between a given entity or location and the city of Varanasi.
-
B.
distanceToAyodhya
Indicates the measured spatial distance between a given entity’s location and the location of Ayodhya.
-
C.
distanceFromPatna
Indicates the spatial distance between a given location and the city of Patna.
-
D.
distanceToBodhGaya
Indicates the spatial distance between a given entity and the location of Bodh Gaya.
-
E.
distanceFromVrindavan
Indicates the spatial distance between a given entity and the location Vrindavan.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:54 p.m.