Triple
T3615525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ranikot Fort |
E76590
|
entity |
| Predicate | distanceFromHyderabad |
P31800
|
FINISHED |
| Object | approximately 90 kilometers |
—
|
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 kilometers | Statement: [Ranikot Fort, distanceFromHyderabad, approximately 90 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromHyderabad Context triple: [Ranikot Fort, distanceFromHyderabad, approximately 90 kilometers]
-
A.
distanceToHyderabad
chosen
Indicates the spatial distance between a given entity’s location and the city of Hyderabad.
-
B.
distanceFromBangalore
Indicates the spatial distance separating a given entity or location from Bangalore.
-
C.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
D.
distanceFromChennai
Indicates the spatial distance between a given entity or location and the city of Chennai.
-
E.
distanceFromKolkata
Indicates the spatial distance between a given location and the city of Kolkata.
- 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_69ad85dae2fc81908d1ceadbc6af0089 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc27b5e008190a72a8dab7d736e64 |
completed | March 8, 2026, 6:39 p.m. |
| PD | Predicate disambiguation | batch_69adb83f1e4c8190ab501c1c05b14c08 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.