Triple
T21198351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iaiá Garcia |
E522384
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object | Iaiá Garcia (character) |
—
|
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: Iaiá Garcia (character) | Statement: [Iaiá Garcia, hasMainCharacter, Iaiá Garcia (character)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iaiá Garcia (character) Context triple: [Iaiá Garcia, hasMainCharacter, Iaiá Garcia (character)]
-
A.
Tita De la Garza
Tita De la Garza is the passionate, magically gifted protagonist of Laura Esquivel’s novel "Like Water for Chocolate," whose emotions infuse the food she cooks.
-
B.
Iaiá Garcia
chosen
Iaiá Garcia is a 19th-century Brazilian novel by Machado de Assis that explores themes of love, class, and social ambition in Rio de Janeiro.
-
C.
Rosita
Rosita is a shy but talented pig and devoted mother who becomes a standout performer in the animated musical film "Sing."
-
D.
Rosita
Rosita is a bilingual, turquoise monster Muppet on Sesame Street known for introducing Spanish language and Latino culture to the show.
-
E.
Rosita
Rosita is a companion character who appears alongside the Doctor in the "Doctor Who" special episode "The Next Doctor."
- 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7333d6dec8190bbc66a71b31ea559 |
completed | April 21, 2026, 8:20 a.m. |
Created at: April 16, 2026, 3:16 p.m.