Triple
T8424802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macy Gray |
E198954
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Natalie |
E589569
|
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: Natalie | Statement: [Macy Gray, givenName, Natalie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Natalie Context triple: [Macy Gray, givenName, Natalie]
-
A.
Natalie
Natalie is a fictional character from the romantic comedy universe of "Love Actually," appearing in the charity sequel short film "Red Nose Day Actually."
-
B.
Natalie
Natalie is the given name of Natalie Evans, Baroness Evans of Bowes Park, a British Conservative politician and life peer.
-
C.
Natalie
Natalie is the central protagonist of the science fiction thriller film "The Darkest Hour," around whom the story’s alien-invasion survival plot revolves.
-
D.
Natalie
chosen
Natalie is a feminine given name of Latin origin, commonly associated with the meaning "birthday of the Lord" or "Christmas Day."
-
E.
Natalie
Natalie is the central protagonist of the British film "Life Is Sweet," around whom the story’s family and everyday struggles revolve.
- 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb85a0d04481908a5da908cafeceaa |
completed | March 31, 2026, 8:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce399384ec81908d3ace592d1e3aea |
completed | April 2, 2026, 9:40 a.m. |
Created at: March 30, 2026, 6:07 p.m.