Triple
T24382202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Athmuqam |
E614639
|
entity |
| Predicate | distanceFromMuzaffarabad_km |
P155995
|
FINISHED |
| Object | approximately 80 |
—
|
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 80 | Statement: [Athmuqam, distanceFromMuzaffarabad_km, approximately 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMuzaffarabad_km Context triple: [Athmuqam, distanceFromMuzaffarabad_km, approximately 80]
-
A.
distanceFromIslamabad
Indicates the spatial distance between a given location and the city of Islamabad.
-
B.
distanceFromKarachi
Indicates the measured spatial distance between a given entity’s location and the city of Karachi.
-
C.
distanceToLahore_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Lahore.
-
D.
distanceFrom Peshawar
Indicates the spatial distance between a given location or entity and the city of Peshawar.
-
E.
distanceFromLashkarGah
Indicates the measured distance between a given location and the city of Lashkar Gah.
- 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_69e2d7e362e481909e32fe4ef8269d4f |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f293dcd50c8190b242f68198343caa |
completed | April 29, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69f287c4a2b48190b80fb7a3c0e9b018 |
completed | April 29, 2026, 10:35 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:03 a.m.