Triple
T2250547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adam Driver |
E49605
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | Annette |
E170799
|
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: Annette | Statement: [Adam Driver, knownFor, Annette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annette Context triple: [Adam Driver, knownFor, Annette]
-
A.
Annette
chosen
Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
-
B.
Gigi
Gigi is a 1958 American musical romantic comedy film, directed by Vincente Minnelli, that won multiple Academy Awards and is celebrated for its lavish production and memorable score.
-
C.
Gigi
Gigi was the affectionate nickname of Gianna Bryant, the late daughter of NBA legend Kobe Bryant who was known for her own promising basketball talent.
-
D.
Malena
Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
-
E.
Starlette
Starlette is a lightweight, high-performance ASGI framework for building asynchronous web applications and services in Python.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc11b61888190af3b11b87dc8e0dc |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b1bc424819087b2ce9a6256a180 |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.