Triple
T22384317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beylik of Saruhan |
E553354
|
entity |
| Predicate | notableCity |
P2813
|
FINISHED |
| Object | Foça |
—
|
NE NERFINISHED |
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: Foça | Statement: [Beylik of Saruhan, notableCity, Foça]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Foça Context triple: [Beylik of Saruhan, notableCity, Foça]
-
A.
Foça
chosen
Foça is a coastal town in western Turkey on the Aegean Sea, known for its historic harbor, ancient ruins, and traditional stone houses.
-
B.
Foé
Foé is the surname of Marc-Vivien Foé, a renowned Cameroonian professional footballer who played as a midfielder in Europe and for the Cameroon national team.
-
C.
Foetz
Foetz is a small town in southwestern Luxembourg, known for its commercial and industrial areas within the commune of Mondercange.
-
D.
Foxx
Foxx is a surname most famously associated with Jimmie Foxx, a Hall of Fame American baseball player and one of the sport’s greatest power hitters.
-
E.
Foody
Foody is the anthropomorphic, fruit-and-vegetable-themed character that served as the official mascot of Expo 2015 in Milan.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1582e58dc8190a2ad6b10c9d1f951 |
completed | April 29, 2026, 1 a.m. |
Created at: April 16, 2026, 8:45 p.m.