Triple
T17471851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Huron |
E425433
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Miguel Briseño |
—
|
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: Miguel Briseño | Statement: [Lord Huron, hasMember, Miguel Briseño]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miguel Briseño Context triple: [Lord Huron, hasMember, Miguel Briseño]
-
A.
Miguel Briseño
chosen
Miguel Briseño is a musician best known as a member of the American indie folk band Lord Huron.
-
B.
Miguel Briseño
Miguel Briseño is a performer known for his role in the production of "Fool for Love."
-
C.
Alfredo Flores
Alfredo Flores is a music video director best known for his frequent collaborations with major pop and R&B artists, particularly Justin Bieber.
-
D.
Enrique Cruz
Enrique Cruz is a kindhearted airport food-service worker in the film "The Terminal" who befriends stranded traveler Viktor Navorski.
-
E.
Guillermo Magaña
Guillermo Magaña is a person notable enough to be recognized as a bearer of the surname Magaña, though specific widely known public details about him are not clearly established.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451b8a51081908d94bebe2417e3d3 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.