Triple
T15835535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carol Browner |
E383974
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Browner |
E1080960
|
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: Browner | Statement: [Carol Browner, familyName, Browner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Browner Context triple: [Carol Browner, familyName, Browner]
-
A.
Browner
chosen
Browner is a surname most notably associated with Namiko Love Browner, the daughter of R&B singer Jhené Aiko.
-
B.
I-Rod
I-Rod is the nickname of Iván Rodríguez, a Hall of Fame Major League Baseball catcher renowned for his defensive excellence and strong throwing arm.
-
C.
Ryan Brown
Ryan Brown is a musician best known for his past role as a member of the rock band Papa Roach.
-
D.
Ryan Brown
Ryan Brown is a film editor known for his work on the movie "Horse Girl."
-
E.
Ryan Brown
Ryan Brown is a comic book colorist known for his work on the supernatural action series *The Tower Chronicles*.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e0e1cc8190851b30b03cf9c9b8 |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa137be2c81909c8f04b5cc1a5b21 |
completed | May 9, 2026, 9:03 p.m. |
Created at: April 10, 2026, 4:49 a.m.