Triple
T22425415
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikaela Spielberg |
E554355
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mikaela |
—
|
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: Mikaela | Statement: [Mikaela Spielberg, givenName, Mikaela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mikaela Context triple: [Mikaela Spielberg, givenName, Mikaela]
-
A.
Mikaela
chosen
Mikaela is a feminine given name most prominently associated with American alpine ski champion Mikaela Shiffrin.
-
B.
Mikaela Banes
Mikaela Banes is a skilled, street-smart mechanic and the primary human female protagonist in the early live-action Transformers films, portrayed by Megan Fox.
-
C.
Makenzie Vega
Makenzie Vega is an American actress best known for her role as Grace Florrick on the television legal drama "The Good Wife."
-
D.
Mia Michaels
Mia Michaels is an Emmy-winning American choreographer renowned for her emotionally powerful contemporary dance works on stage, television, and film.
-
E.
Kayla
Kayla is a central character in the tech-comedy web series "Hacks," known for her over-the-top personality and chaotic presence in the workplace.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a2d4dd88190a156c24b02b1591b |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.