Triple
T11509900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalam |
E272880
|
entity |
| Predicate | distanceFromMingora |
P99868
|
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: [Kalam, distanceFromMingora, approximately 90 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMingora Context triple: [Kalam, distanceFromMingora, approximately 90 kilometers]
-
A.
distanceFrom Peshawar
Indicates the spatial distance between a given location or entity and the city of Peshawar.
-
B.
distanceFromShimla_km
Indicates the physical distance, measured in kilometers, between a given place and Shimla.
-
C.
distanceFromKarachi
Indicates the measured spatial distance between a given entity’s location and the city of Karachi.
-
D.
distanceFromIslamabad
Indicates the spatial distance between a given location and the city of Islamabad.
-
E.
distanceToLahore_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Lahore.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d86db65eb081908613a1002c6a4fb4 |
completed | April 10, 2026, 3:25 a.m. |
| PD | Predicate disambiguation | batch_69d80876e5f0819088cff2e72f773cf6 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:36 p.m.