Triple
T11173348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Tyler |
E264339
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mia Tyler |
E264339
|
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: Mia Tyler | Statement: [Mia Tyler, name, Mia Tyler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Tyler Context triple: [Mia Tyler, name, Mia Tyler]
-
A.
Mia Tyler
chosen
Mia Tyler is an American plus-size model, actress, and television personality, and the daughter of Aerosmith frontman Steven Tyler.
-
B.
Mia Morgan
Mia Morgan is a central character in the romantic comedy-drama film "The Best Man," around whom much of the story’s interpersonal conflict and emotional tension revolves.
-
C.
Mia Michaels
Mia Michaels is an Emmy-winning American choreographer renowned for her emotionally powerful contemporary dance works on stage, television, and film.
-
D.
Mia Dolan
Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
-
E.
Mia Frampton
Mia Frampton is an American actress, best known for her roles in film and television and as the daughter of rock musician Peter Frampton.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e897774c819088ebc7231cebfba6 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4acf79b748190b117355f60c8c015 |
completed | April 19, 2026, 10:22 a.m. |
Created at: April 8, 2026, 9:29 p.m.