Triple
T18524940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amythaon |
E452690
|
entity |
| Predicate | hasDescendant |
P3654
|
FINISHED |
| Object | Bias |
—
|
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: Bias | Statement: [Amythaon, hasDescendant, Bias]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bias Context triple: [Amythaon, hasDescendant, Bias]
-
A.
Bias
chosen
Bias is one of the Epigoni, the legendary second-generation Greek heroes who waged a successful campaign against Thebes to avenge their fathers.
-
B.
Biasion
Biasion is an Italian surname most notably associated with Miki Biasion, a two-time World Rally Champion driver.
-
C.
Biase
Biase is a local government area in southeastern Nigeria known for its diverse ethnic communities and agricultural activities within Cross River State.
-
D.
Prejudice
Prejudice is a preconceived, often unfavorable judgment or opinion about people or situations formed without adequate knowledge, reason, or experience.
-
E.
Bi
Bi is a South Korean singer and actor, better known internationally by his stage name Rain, who gained fame for his K-pop music career and roles in television dramas and films.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e533914e808190a46638de1ce2d57b |
completed | April 19, 2026, 7:57 p.m. |
Created at: April 10, 2026, 11:37 a.m.