Triple
T8841165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colores |
E210392
|
entity |
| Predicate | notableSingle |
P3283
|
FINISHED |
| Object | Blanco |
E173667
|
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: Blanco | Statement: [Colores, notableSingle, Blanco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blanco Context triple: [Colores, notableSingle, Blanco]
-
A.
Blanco
chosen
Blanco is a Spanish-language surname most notably associated with Mexican football legend and politician Cuauhtémoc Blanco.
-
B.
Blanc
Blanc is the surname of Mel Blanc, the legendary American voice actor best known for bringing to life many iconic Looney Tunes characters.
-
C.
La Blanca
La Blanca is a municipality located in the San Marcos Department of western Guatemala, known for its rural character and agricultural activities.
-
D.
Bianco
Bianco is an Italian surname commonly associated with individuals of Italian heritage, including the artist Enrico Bianco.
-
E.
البيضاء
البيضاء هي مدينة ليبية تقع في الجبل الأخضر بشرق البلاد وتعد من المراكز الإدارية والاقتصادية المهمة هناك.
- 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_69ca838967bc8190b46c3c80a2887ea4 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60876c6c8190b1b490e447e1cf4b |
completed | April 1, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf899c5b288190b854acebc9fe33d1 |
completed | April 3, 2026, 9:34 a.m. |
Created at: March 30, 2026, 6:48 p.m.