Triple
T10082644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bryant |
E213940
|
entity |
| Predicate | hasVariant |
P455
|
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: [Bryant, hasVariant, Bryan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bryan Context triple: [Bryant, hasVariant, Bryan]
-
A.
Bryan
chosen
Bryan is a masculine given name of Celtic origin that is widely used in English-speaking countries.
-
B.
Bryan
Bryan is a mid-sized city in Central Texas known for its close association with neighboring College Station and Texas A&M University.
-
C.
Bryan England
Bryan England is a cinematographer best known for his work on the 1991 horror film adaptation of Stephen King's "Sometimes They Come Back."
-
D.
Bryse
Bryse is an alternative spelling of the given name Bryce, typically used as a modern or stylistic variant.
-
E.
Bryan Unkeless
Bryan Unkeless is a film producer known for working on acclaimed movies such as "I, Tonya" and other high-profile Hollywood projects.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd03482d481908b03d35dc2d16395 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b66b256c8190861066f7c19008d2 |
completed | April 5, 2026, 7:22 p.m. |
Created at: March 30, 2026, 9 p.m.