Triple
T15772854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yer Demir Gök Bakır |
E382404
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object | Ortadirek |
E382407
|
NE 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: Ortadirek | Statement: [Yer Demir Gök Bakır, follows, Ortadirek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ortadirek Context triple: [Yer Demir Gök Bakır, follows, Ortadirek]
-
A.
Ortadirek
chosen
Ortadirek is a novel by renowned Turkish author Yaşar Kemal, recognized as part of his influential body of modern Turkish literature.
-
B.
Orhaneli
Orhaneli is a town and district in northwestern Turkey known for its rural character and location within Bursa Province.
-
C.
Ortaca
Ortaca is a town and district in southwestern Turkey known for its proximity to popular Aegean and Mediterranean coastal resorts and natural attractions.
-
D.
Darıca
Darıca is a coastal town and district in northwestern Turkey, situated on the Sea of Marmara and known for its zoo, recreation areas, and proximity to Istanbul.
-
E.
Dirizhon
Dirizhon is a film production company known for producing the movie "Viking."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e051976d248190adddd3db9f758e22 |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff909b467c819097ee87f51d2001da |
completed | May 9, 2026, 7:52 p.m. |
Created at: April 10, 2026, 4:47 a.m.