Triple
T18893316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acun Ilıcalı |
E462146
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Fear Factor Turkey |
—
|
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: Fear Factor Turkey | Statement: [Acun Ilıcalı, notableWork, Fear Factor Turkey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fear Factor Turkey Context triple: [Acun Ilıcalı, notableWork, Fear Factor Turkey]
-
A.
Fear Factor
chosen
Fear Factor is a reality television game show in which contestants compete in extreme and often fear-inducing stunts for cash prizes.
-
B.
Harsiyyet
Harsiyyet is an alternative name for the Harsusi language, a Modern South Arabian language spoken by a small community in Oman.
-
C.
Terror
Terror refers to the period of extreme political violence and repression during the French Revolution, marked by mass executions and authoritarian rule.
-
D.
Atanka
Atanka is an Indian Bengali-language film directed by Tapan Sinha, known for its tense portrayal of fear and moral conflict in a rural setting.
-
E.
Fearing
Fearing is a given name most notably associated with Rufus Fearing Dawes, an American Civil War officer and politician.
- 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c47d392c81909297211c7d7610a1 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 10, 2026, 11:58 a.m.