Triple
T13004638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cher Horowitz |
E322252
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cher |
E92362
|
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: Cher | Statement: [Cher Horowitz, givenName, Cher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cher Context triple: [Cher Horowitz, givenName, Cher]
-
A.
Cher
Cher is a department in central France, named after the Cher River and known for its historic towns, vineyards, and agricultural landscapes.
-
B.
Cher
chosen
Cher is an American singer, actress, and pop culture icon known for her distinctive contralto voice, decades-spanning career, and hits like "Believe" and "If I Could Turn Back Time."
-
C.
Cher
Cher is the four-letter ISO 15924 script code that designates the Cherokee syllabary writing system.
-
D.
Barbara West
Barbara West is an actress known for her role in the acclaimed Australian psychological horror film "The Babadook."
-
E.
Cyndi Grecco
Cyndi Grecco is an American singer best known for performing the upbeat 1970s television theme song "Making Our Dreams Come True" from the sitcom Laverne & Shirley.
- 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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e9a2a448190968833354280e474 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c107f990819093042560b1bc4473 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:47 p.m.