Triple
T3462989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juni Cortez |
E73070
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Carmen Cortez |
E72698
|
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: Carmen Cortez | Statement: [Juni Cortez, relative, Carmen Cortez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carmen Cortez Context triple: [Juni Cortez, relative, Carmen Cortez]
-
A.
Carmen Cortez
chosen
Carmen Cortez is a resourceful young spy and one of the two sibling protagonists in the Spy Kids film series.
-
B.
Juni Cortez
Juni Cortez is a young, tech-savvy secret agent and one of the sibling protagonists in the Spy Kids film series.
-
C.
Carmen Molina
Carmen Molina was a Mexican actress and singer known for her musical performances in mid-20th-century films, including a notable appearance in Disney productions.
-
D.
Amada Cruz
Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
-
E.
Carmen Basilio
Carmen Basilio was an American professional boxer renowned for his rugged, aggressive style and for winning world titles in both the welterweight and middleweight divisions during the 1950s.
- 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_69ad85b224d481908ff8be51338d24ff |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb0b73f481908c9f8d2b9b9bbbaf |
completed | March 8, 2026, 6:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373aaff688190b9f12a7042055a1c |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:17 p.m.