Triple
T16425027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierre Nicole |
E398920
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Nicole |
E476645
|
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: Nicole | Statement: [Pierre Nicole, familyName, Nicole]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicole Context triple: [Pierre Nicole, familyName, Nicole]
-
A.
Nicole
chosen
Nicole is a feminine given name of Greek origin meaning "victory of the people," commonly used in many English- and French-speaking countries.
-
B.
Nicole
Nicole is a fictional character from the American sitcom "The Gregory Hines Show."
-
C.
Nicole
Nicole is a fictional character played by English actress Kelly Reilly, known from her work in film and television dramas.
-
D.
Nicole
Nicole is a fictional character portrayed by Canadian actress Lindy Booth.
-
E.
Nicole
Nicole is a central character in the dark comedy-drama film "Hesher," serving as a key emotional anchor in the story’s exploration of grief and unconventional relationships.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328f9da9081908dadbdac4b2d38ec |
completed | April 18, 2026, 6:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c7273e48190b0668948141cf30b |
completed | May 10, 2026, 8:06 a.m. |
Created at: April 10, 2026, 5:09 a.m.