Triple
T4559849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Girls Revolt |
E120565
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Dana Calvo |
E452267
|
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: Dana Calvo | Statement: [The Good Girls Revolt, executiveProducer, Dana Calvo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dana Calvo Context triple: [The Good Girls Revolt, executiveProducer, Dana Calvo]
-
A.
Dana Calvo
chosen
Dana Calvo is an American television writer and producer best known for creating the period drama series "Good Girls Revolt."
-
B.
Alexis Chávez
Alexis Chávez is an Argentine Paralympic middle-distance runner known for competing in international para-athletics events.
-
C.
Nadine Velazquez
Nadine Velazquez is an American actress and model best known for her roles in the sitcom "My Name Is Earl" and the film "Flight."
-
D.
Rachel Salas
Rachel Salas is a central character in the science-fiction film "In Time," portrayed as the wealthy and protective mother of Sylvia Weis.
-
E.
Katherine Martorell
Katherine Martorell is a Chilean lawyer and politician known for her roles in public security and government, associated with the right-wing political sector.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd582b871c8190be0b70c76d639000 |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3aa3b9081908984777207f4040e |
completed | March 20, 2026, 11:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.