Triple
T15275436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akyurt |
E365125
|
entity |
| Predicate | distanceTo_AnkaraCityCenter_km |
P106572
|
FINISHED |
| Object | approximately 30 |
—
|
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 30 | Statement: [Akyurt, distanceTo_AnkaraCityCenter_km, approximately 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceTo_AnkaraCityCenter_km Context triple: [Akyurt, distanceTo_AnkaraCityCenter_km, approximately 30]
-
A.
distanceToAnkara
chosen
Indicates the spatial distance between a given entity’s location and the city of Ankara.
-
B.
distanceFromKayseri
Indicates the spatial distance between a given entity and the location of Kayseri.
-
C.
distanceToIstanbulApproxKm
Indicates the approximate distance, measured in kilometers, between a given place and Istanbul.
-
D.
distanceFromAlmaty_km
Indicates the distance, measured in kilometers, between a given place or object and the city of Almaty.
-
E.
distanceToKyzylorda
Indicates the spatial distance between a given entity or location and the city of Kyzylorda.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00952731c8190bf6a5e6e10c95b94 |
completed | April 15, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:14 a.m.