Triple
T19035377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Mazilier |
E465853
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Paquita |
—
|
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: Paquita | Statement: [Joseph Mazilier, notableWork, Paquita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paquita Context triple: [Joseph Mazilier, notableWork, Paquita]
-
A.
Paquita
chosen
Paquita is a 19th-century classical ballet, originally choreographed by Marius Petipa, renowned for its virtuosic dances and enduring presence in the ballet repertoire.
-
B.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
C.
Blanquita
Blanquita is the namesake figure—likely an influential woman or performer—after whom Mexico City’s historic Teatro Blanquita was named.
-
D.
Carmencita
Carmencita is a Spanish feminine given name, commonly used as an affectionate diminutive of Carmen.
-
E.
Dona Paula
Dona Paula is a popular coastal tourist destination near Panaji in Goa, India, known for its scenic sea views, romantic legends, and water sports.
- 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_69d8dd0359648190bc2a9202c5cf29d2 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d7438f748190912c28912e6b97a6 |
completed | April 20, 2026, 7:35 a.m. |
Created at: April 10, 2026, 12:02 p.m.