Triple
T18287265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traveling Wilburys Vol. 1 |
E438014
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Margarita |
—
|
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: Margarita | Statement: [Traveling Wilburys Vol. 1, hasPart, Margarita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margarita Context triple: [Traveling Wilburys Vol. 1, hasPart, Margarita]
-
A.
Margarita
Margarita is a feminine given name of Spanish origin, equivalent to "Margaret" in English.
-
B.
Margarita
chosen
Margarita is a classic tequila-based cocktail typically made with lime juice and orange liqueur, often served in a salt-rimmed glass.
-
C.
One Margarita
"One Margarita" is a popular country party anthem by American singer Luke Bryan, known for its laid-back beach vibe and catchy, summer-themed lyrics.
-
D.
Palomas
Palomas is a small municipality located in the Tierra de Barros comarca of the Extremadura region in western Spain.
-
E.
Tequila and Bonetti
Tequila and Bonetti is an early-1990s American comedy-drama television series about a New York cop partnered with a talking police dog in a California beach town.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500faf62081908f16ebee0a195ba2 |
completed | April 19, 2026, 4:21 p.m. |
Created at: April 10, 2026, 10:35 a.m.