Triple
T7043378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fenerbahçe SK |
E163569
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | Fener |
E125045
|
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: Fener | Statement: [Fenerbahçe SK, shortName, Fener]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fener Context triple: [Fenerbahçe SK, shortName, Fener]
-
A.
Fener
chosen
Fener is a historic neighborhood in Istanbul, Turkey, known for its Greek Orthodox heritage and landmarks such as the Ecumenical Patriarchate and traditional Ottoman-era houses.
-
B.
Fasa
Fasa is a city in Iran’s Fars Province known as a regional agricultural and commercial center with historical significance.
-
C.
Fenerbahçe Rowing
Fenerbahçe Rowing is the rowing department of the Turkish multi-sport club Fenerbahçe SK, competing in national and international rowing events.
-
D.
Karşıyaka
Karşıyaka is a populous coastal district of İzmir, Turkey, known for its vibrant urban life, historic neighborhoods, and strong local sports culture.
-
E.
Murça
Murça is a small municipality in northern Portugal, known for its wine production and location within the Douro region.
- 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_69c6885e7c1c8190be32a8f79ab4e0cf |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e235a2e08190bb049ee6e719f0f9 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7943fe4fc819087bbcc724deed80a |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:36 p.m.