Triple
T7035546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Bryan Martin |
E163372
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object | Bryan |
E186599
|
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: Bryan | Statement: [Thomas Bryan Martin, middleName, Bryan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bryan Context triple: [Thomas Bryan Martin, middleName, Bryan]
-
A.
Bryan
Bryan is a mid-sized city in Central Texas known for its close association with neighboring College Station and Texas A&M University.
-
B.
Bryan
chosen
Bryan is a masculine given name of Celtic origin that is widely used in English-speaking countries.
-
C.
Bryse
Bryse is an alternative spelling of the given name Bryce, typically used as a modern or stylistic variant.
-
D.
Bryan Unkeless
Bryan Unkeless is a film producer known for working on acclaimed movies such as "I, Tonya" and other high-profile Hollywood projects.
-
E.
Bryan Burk
Bryan Burk is an American film and television producer best known for his collaborations with J.J. Abrams on projects such as Lost, Star Trek, and Mission: Impossible.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e220508c8190b8950cf38280b8c2 |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775a211f88190afe5ed466abcac7a |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:36 p.m.