Triple
T16829482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Stefano |
E409109
|
entity |
| Predicate | distanceFromIstanbul |
P21189
|
FINISHED |
| Object | approximately 11 kilometers southwest |
—
|
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 11 kilometers southwest | Statement: [San Stefano, distanceFromIstanbul, approximately 11 kilometers southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromIstanbul Context triple: [San Stefano, distanceFromIstanbul, approximately 11 kilometers southwest]
-
A.
distanceToIstanbulApproxKm
chosen
Indicates the approximate distance, measured in kilometers, between a given place and Istanbul.
-
B.
distanceToAnkara
Indicates the spatial distance between a given entity’s location and the city of Ankara.
-
C.
distanceFromKayseri
Indicates the spatial distance between a given entity and the location of Kayseri.
-
D.
distanceToİzmir
Indicates the spatial distance between a given entity and the city of İzmir.
-
E.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b315dbbc81908a1c83069e058770 |
completed | April 18, 2026, 4:36 p.m. |
| PD | Predicate disambiguation | batch_69e32b814b188190aee525f8779203cd |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:23 a.m.