Triple
T19973003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Garber |
E480124
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Garber |
—
|
NE NERFINISHED |
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: Garber | Statement: [Daniel Garber, familyName, Garber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garber Context triple: [Daniel Garber, familyName, Garber]
-
A.
Garber
chosen
Garber is a surname most notably associated with Canadian actor and singer Victor Garber, known for his work in film, television, and theater.
-
B.
Gabbs
Gabbs is a small, remote town in central Nevada known historically for its mining activities and desert surroundings.
-
C.
Gabler
Gabler is a surname most notably associated with Milt Gabler, an influential American record producer and songwriter in jazz and popular music.
-
D.
Nevin
Nevin is a surname most notably associated with Phil Nevin, a former Major League Baseball player and manager.
-
E.
Farris
Farris is a surname most notably associated with Christine King Farris, an American educator, author, and the elder sister of Martin Luther King Jr.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bca94c0819095c902a411c4c4b8 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.