Triple
T12770497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 9 September |
E305235
|
entity |
| Predicate | hasNameInTurkish |
P15502
|
FINISHED |
| Object | Dokuz Eylül |
E63429
|
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: Dokuz Eylül | Statement: [9 September, hasNameInTurkish, Dokuz Eylül]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dokuz Eylül Context triple: [9 September, hasNameInTurkish, Dokuz Eylül]
-
A.
Dokuz Eylul University
chosen
Dokuz Eylul University is a major public research university in İzmir, Turkey, known for its wide range of academic programs and significant regional influence.
-
B.
Ondokuzmayıs
Ondokuzmayıs is a coastal district and town in Turkey’s Samsun Province, situated along the Black Sea.
-
C.
Tevfikiye
Tevfikiye is a village in northwestern Turkey located close to the archaeological site of Hisarlik, widely identified with ancient Troy.
-
D.
Ülker
Ülker is a major Turkish food company best known for its wide range of confectionery and snack products.
-
E.
Mehmet Akif Ersoy University
Mehmet Akif Ersoy University is a Turkish public university named in honor of the renowned poet and national figure Mehmet Akif Ersoy.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df4b36c81909bcc913dd5e535f8 |
completed | April 10, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684f9ba848190b680d730b6a3b972 |
completed | May 2, 2026, 11:12 p.m. |
Created at: April 9, 2026, 5:28 p.m.