Triple
T12792780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shigar Fort |
E305810
|
entity |
| Predicate | distanceFromSkardu |
P102189
|
FINISHED |
| Object | approximately 32 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 32 kilometres | Statement: [Shigar Fort, distanceFromSkardu, approximately 32 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSkardu Context triple: [Shigar Fort, distanceFromSkardu, approximately 32 kilometres]
-
A.
distanceToSkardu
chosen
Indicates the measured or specified distance between a given location or object and Skardu.
-
B.
distanceFrom Peshawar
Indicates the spatial distance between a given location or entity and the city of Peshawar.
-
C.
distanceFromIslamabad
Indicates the spatial distance between a given location and the city of Islamabad.
-
D.
distanceFromKarachi
Indicates the measured spatial distance between a given entity’s location and the city of Karachi.
-
E.
distanceToHunza
Indicates the spatial distance between a given entity and the location of Hunza.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6b55248190ab938e69eb263612 |
completed | April 10, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69d9640ba0688190973e4e7ec8d4a8e0 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:30 p.m.