Triple
T15566381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Geena Davis Show |
E371124
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Makenzie Vega |
E781256
|
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: Makenzie Vega | Statement: [The Geena Davis Show, hasCastMember, Makenzie Vega]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makenzie Vega Context triple: [The Geena Davis Show, hasCastMember, Makenzie Vega]
-
A.
Makenzie Vega
chosen
Makenzie Vega is an American actress best known for her role as Grace Florrick on the television legal drama "The Good Wife."
-
B.
Mikaela
Mikaela is a feminine given name most prominently associated with American alpine ski champion Mikaela Shiffrin.
-
C.
Alexa Vega
Alexa Vega is an American actress and singer best known for playing Carmen Cortez in the Spy Kids film series.
-
D.
Maya Wilkes
Maya Wilkes is a central character on the sitcom "Girlfriends," known for her sharp wit, strong opinions, and journey balancing friendship, family, and career.
-
E.
Makenzie Leigh
Makenzie Leigh is an American actress known for her roles in film and television, including a supporting part in the war drama "Billy Lynn's Long Halftime Walk."
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ddd753c8190b51eaef433258081 |
completed | April 16, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff56bf8cac81909886de5b82849cb2 |
completed | May 9, 2026, 3:46 p.m. |
Created at: April 10, 2026, 4:10 a.m.