Triple
T31398772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Türkmenabat |
E800933
|
entity |
| Predicate | roadDistanceToAshgabat_km |
P171822
|
FINISHED |
| Object | approximately 600 |
—
|
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 600 | Statement: [Türkmenabat, roadDistanceToAshgabat_km, approximately 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadDistanceToAshgabat_km Context triple: [Türkmenabat, roadDistanceToAshgabat_km, approximately 600]
-
A.
distanceFromAshgabat
Indicates the measured spatial distance between a given location and the city of Ashgabat.
-
B.
distanceFromBaku
Indicates the measured distance between a given place or object and the city of Baku.
-
C.
distanceToStepanakert_km
Indicates the physical distance, measured in kilometers, between a given location and Stepanakert.
-
D.
roadDistanceToTashkent
Indicates the distance between an entity and Tashkent measured along the road network rather than in a straight line.
-
E.
distanceToKabul
Indicates the measured spatial distance between a given location or entity and the city of Kabul.
- 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_69f224ea9998819086ae2e4f4f4091c8 |
completed | April 29, 2026, 3:34 p.m. |
| NER | Named-entity recognition | batch_69f6a5f71b2c8190aade8a83f465be0c |
completed | May 3, 2026, 1:33 a.m. |
| PD | Predicate disambiguation | batch_69f69fe66df08190958558d63ee623d9 |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a5f656ec81909e02b0b873303adf |
completed | May 3, 2026, 1:33 a.m. |
Created at: April 29, 2026, 9:19 p.m.