Triple
T21393114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carol Haney |
E527708
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Carol |
—
|
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: Carol | Statement: [Carol Haney, givenName, Carol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carol Context triple: [Carol Haney, givenName, Carol]
-
A.
Carol
chosen
Carol is a feminine given name commonly used in English-speaking countries, often associated with figures in entertainment and literature.
-
B.
Carol
Carol is a critically acclaimed 2015 romantic drama film, directed by Todd Haynes and starring Cate Blanchett and Rooney Mara, about a forbidden love affair between two women in 1950s New York.
-
C.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
D.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
E.
Nancy
Nancy is a central character in the meta-horror comedy film "The Final Girls," portrayed as a sweet but archetypal 1980s slasher-movie camp counselor who becomes crucial to the story’s emotional core.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b1165a508190a72c008117675711 |
completed | April 22, 2026, 11:29 a.m. |
Created at: April 16, 2026, 5:13 p.m.