Triple
T14773142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maine Mariners |
E347185
|
entity |
| Predicate | generalManager |
P537
|
FINISHED |
| Object | Daniel Briere |
E54760
|
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: Daniel Briere | Statement: [Maine Mariners, generalManager, Daniel Briere]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Briere Context triple: [Maine Mariners, generalManager, Daniel Briere]
-
A.
Brice Gonzalez
Brice Gonzalez is a young American actor best known for his role on the NBC sitcom "Lopez vs Lopez."
-
B.
Chris Durand
Chris Durand is a stuntman and actor best known for playing the iconic slasher villain Michael Myers in the horror film "Halloween H20: 20 Years Later."
-
C.
Marc Calixte
Marc Calixte is a screenwriter known for his work on the romantic comedy film "The Perfect Holiday."
-
D.
Jeremie Berrebi
Jeremie Berrebi is a French-Israeli entrepreneur and investor best known as the co-founder of the prolific early-stage investment firm Kima Ventures.
-
E.
Daniel Brière
chosen
Daniel Brière is a former NHL center turned ice hockey executive who became the general manager of the Philadelphia Flyers.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec81485e08190be35baafcf22b6f2 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24b41df881908119e183b8299c48 |
completed | May 8, 2026, 6 p.m. |
Created at: April 10, 2026, 1:31 a.m.