Triple
T6147875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheryl Miller |
E137124
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cheryl |
E329120
|
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: Cheryl | Statement: [Cheryl Miller, givenName, Cheryl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cheryl Context triple: [Cheryl Miller, givenName, Cheryl]
-
A.
Cheryl
chosen
Cheryl is a British singer and television personality best known for her successful solo career and high-profile role as a judge on the UK version of The X Factor.
-
B.
Chloe
Chloe is the birth name of Nobel Prize–winning American novelist Toni Morrison, renowned for her powerful explorations of African American life and history.
-
C.
Chloe
Chloe is a sarcastic, food-loving, overweight gray tabby cat from the animated film "The Secret Life of Pets."
-
D.
Chloe
Chloe is an epithet of the Greek goddess Demeter, highlighting her aspect as the bringer of new green growth and flourishing vegetation.
-
E.
Cherie
Cherie is the naive yet determined young woman who becomes the romantic focus of the cowboy in the classic stage play and film "Bus Stop."
- 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_69c008a2c6308190a56519b22d55d083 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05ce07fb081909278088e9e2e2959 |
completed | March 22, 2026, 9:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c13603730881909b3991c7262509b5 |
completed | March 23, 2026, 12:45 p.m. |
Created at: March 22, 2026, 4:16 p.m.