Triple
T14766157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Necessary Roughness |
E347000
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Paloma |
E200070
|
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: Paloma | Statement: [Necessary Roughness, hasCharacter, Paloma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paloma Context triple: [Necessary Roughness, hasCharacter, Paloma]
-
A.
Paloma
chosen
Paloma is a feminine given name of Spanish origin meaning "dove," famously borne by designer Paloma Picasso.
-
B.
Paloma
Paloma is a popular Mexican tequila-based cocktail typically made with grapefruit soda or juice and lime, known for its refreshing, citrusy flavor.
-
C.
Blanquita
Blanquita is the namesake figure—likely an influential woman or performer—after whom Mexico City’s historic Teatro Blanquita was named.
-
D.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
-
E.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f576c881909da70627f5897c94 |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24b1ff0c81908d5dffbaf86c3ca3 |
completed | May 8, 2026, 6 p.m. |
Created at: April 10, 2026, 1:30 a.m.