Triple
T16004264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brooklyn Decker |
E388172
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Decker |
E576164
|
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: Decker | Statement: [Brooklyn Decker, familyName, Decker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Decker Context triple: [Brooklyn Decker, familyName, Decker]
-
A.
Decker
chosen
Decker is a middle name borne by Ellnora Decker Krannert, an American philanthropist known for her support of the arts and education.
-
B.
Neudecker
Neudecker is a German-origin surname borne by various individuals, including contemporary figures such as artist Lianne Neudecker.
-
C.
Jack Deerson
Jack Deerson is a cinematographer best known for his work on the 1971 road movie "Two-Lane Blacktop."
-
D.
Reeser
Reeser is a surname most notably associated with American actress Autumn Reeser, known for her roles in television and film.
-
E.
Daboll
Daboll is a surname most prominently associated with Brian Daboll, a professional American football coach in the National Football League.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157fe776c81908f7bf29ef064a6ba |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3dec0c0819099b9007ad0fc6fb4 |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:55 a.m.