Triple
T14268101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angie Cepeda |
E353703
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Angélica |
E1056673
|
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: Angélica | Statement: [Angie Cepeda, givenName, Angélica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angélica Context triple: [Angie Cepeda, givenName, Angélica]
-
A.
Angélica
chosen
Angélica is a feminine given name of Romance-language origin, commonly used in Spanish- and Portuguese-speaking countries.
-
B.
Maria de Luna
Maria de Luna was a powerful 14th–15th century Aragonese noblewoman and queen consort renowned for her political influence, capable governance, and patronage of religious and charitable works.
-
C.
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.
-
D.
Rosita
Rosita is a shy but talented pig and devoted mother who becomes a standout performer in the animated musical film "Sing."
-
E.
Rosita
Rosita is a bilingual, turquoise monster Muppet on Sesame Street known for introducing Spanish language and Latino culture to the show.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6358c2288190ac1fd26e688a605d |
completed | April 14, 2026, 3:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd32682a0481908918570a778e185a |
completed | May 8, 2026, 12:46 a.m. |
Created at: April 10, 2026, 1:10 a.m.