Triple
T15024950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Celâl Bayar |
E378184
|
entity |
| Predicate | educatedAt |
P5
|
FINISHED |
| Object | İnegöl Rüştiyesi |
E983081
|
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: İnegöl Rüştiyesi | Statement: [Celâl Bayar, educatedAt, İnegöl Rüştiyesi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: İnegöl Rüştiyesi Context triple: [Celâl Bayar, educatedAt, İnegöl Rüştiyesi]
-
A.
Turkish Café
"Turkish Café" is a 1914 Expressionist painting by German artist August Macke that depicts a vibrant, stylized café scene inspired by his travels in North Africa.
-
B.
İnegöl
chosen
İnegöl is a town and district in northwestern Turkey known for its furniture industry and distinctive İnegöl köfte (meatballs).
-
C.
Ilgın
Ilgın is a town and district in Turkey’s Konya Province, known for its thermal springs and agricultural activities.
-
D.
Ortaköy
Ortaköy is a lively Bosphorus-side neighborhood in Istanbul known for its waterfront mosque, cafes, and views of the Bosporus Bridge.
-
E.
Ortaköy
Ortaköy is a district and town in central Turkey’s Aksaray Province, known for its rural character and agricultural economy.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7de117c8190a1b9fa8d1602057e |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd499108190b803c6afc0fa00bc |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:56 a.m.