Triple
T7856461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benazir Bhutto International Airport |
E182386
|
entity |
| Predicate | distanceFromIslamabadCityCenter |
P45681
|
FINISHED |
| Object | approximately 15 km |
—
|
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 15 km | Statement: [Benazir Bhutto International Airport, distanceFromIslamabadCityCenter, approximately 15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromIslamabadCityCenter Context triple: [Benazir Bhutto International Airport, distanceFromIslamabadCityCenter, approximately 15 km]
-
A.
distanceFromIslamabad
chosen
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.
distanceFromChandigarh_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Chandigarh.
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a75de548190af5653409a3b3881 |
completed | March 31, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69cae92180f88190ae3d44c3de7adc93 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:52 p.m.