Triple
T12117655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akbarpur |
E288606
|
entity |
| Predicate | distanceToAyodhya |
P103374
|
FINISHED |
| Object | approximately 60 kilometres |
—
|
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 60 kilometres | Statement: [Akbarpur, distanceToAyodhya, approximately 60 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAyodhya Context triple: [Akbarpur, distanceToAyodhya, approximately 60 kilometres]
-
A.
distanceFromMathura
Indicates the spatial distance between a given entity or location and the city of Mathura.
-
B.
distanceFromVaranasi
Indicates the measured or specified distance between a given entity or location and the city of Varanasi.
-
C.
distanceFromPatna
Indicates the spatial distance between a given location and the city of Patna.
-
D.
distanceToDelhiApproxKm
Indicates the approximate distance, measured in kilometers, between a given entity’s location and Delhi.
-
E.
distanceToDelhiByRoad_km
Indicates the road travel distance, measured in kilometers, from a given place to Delhi.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916481a008190ae66677b9e6dd961 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:49 p.m.