Triple
T21426817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coro |
E528577
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Coro |
—
|
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: Coro | Statement: [Coro, title, Coro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coro Context triple: [Coro, title, Coro]
-
A.
Coro
Coro is a large-scale choral-orchestral composition by Luciano Berio that intricately blends voices and instruments in a highly experimental, texturally rich sound world.
-
B.
Coro
chosen
Coro is a historic coastal city in northwestern Venezuela known for its well-preserved Spanish colonial architecture and status as a UNESCO World Heritage Site.
-
C.
CoR
CoR is the commonly used abbreviation for the European Committee of the Regions, the EU’s advisory body representing local and regional authorities.
-
D.
Canto
Canto is a jazz album by saxophonist and composer Charles Lloyd, noted for its atmospheric, spiritually infused improvisations and lyrical ensemble interplay.
-
E.
Conga
"Conga" is a 1985 Latin pop-dance hit by Gloria Estefan and Miami Sound Machine that popularized Latin rhythms in mainstream American pop music.
- 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_69e0c455f3688190810bc96365791b0f |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee813db52c8190ac933bc6ec4dbf77 |
completed | April 26, 2026, 9:18 p.m. |
Created at: April 16, 2026, 5:49 p.m.