Triple
T33527571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sadda |
E858682
|
entity |
| Predicate | roadDistanceToParachinar_km |
P177070
|
FINISHED |
| Object | approximately 50 |
—
|
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 50 | Statement: [Sadda, roadDistanceToParachinar_km, approximately 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadDistanceToParachinar_km Context triple: [Sadda, roadDistanceToParachinar_km, approximately 50]
-
A.
distanceFromKarachi
Indicates the measured spatial distance between a given entity’s location and the city of Karachi.
-
B.
distanceFrom Peshawar
Indicates the spatial distance between a given location or entity and the city of Peshawar.
-
C.
distanceFromLashkarGah
Indicates the measured distance between a given location and the city of Lashkar Gah.
-
D.
distanceToSkardu
Indicates the measured or specified distance between a given location or object and Skardu.
-
E.
roadDistanceToQuettaInKilometres
Indicates the distance in kilometers between an entity and Quetta when traveling by road.
- 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_69f349781c6c819082c516b260efe7e2 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f85ca7c0819098069384b0d1ca6a |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f814fcf48190ae4504154d1b2c05 |
completed | May 3, 2026, 7:24 a.m. |
Created at: May 1, 2026, 1:39 a.m.