Triple
T11738371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shivneri Fort |
E279087
|
entity |
| Predicate | distanceFromPune |
P101083
|
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: [Shivneri Fort, distanceFromPune, approximately 90 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromPune Context triple: [Shivneri Fort, distanceFromPune, approximately 90 kilometers]
-
A.
distanceFromBengaluru
Indicates the measured spatial distance between a given entity’s location and the city of Bengaluru.
-
B.
distanceFromMumbaiApproxKm
Indicates the approximate physical distance, measured in kilometers, between a given location and Mumbai.
-
C.
distanceFromNashik
Indicates the spatial distance between a given entity or location and the city of Nashik.
-
D.
distanceFromPanaji
Indicates the spatial distance between a given location and the city of Panaji.
-
E.
distanceFromChennai
Indicates the spatial distance between a given entity or location and the city of Chennai.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4ef1c4881909ad36dc27b1fe193 |
completed | April 10, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69d88a7f51248190bf492bd7509b5413 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:41 p.m.