Triple
T6209536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kathleen Blanco |
E138830
|
entity |
| Predicate | familyName |
P18
|
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: [Kathleen Blanco, familyName, Blanco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blanco Context triple: [Kathleen Blanco, familyName, 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.
البيضاء
البيضاء هي مدينة ليبية تقع في الجبل الأخضر بشرق البلاد وتعد من المراكز الإدارية والاقتصادية المهمة هناك.
-
E.
Balderas
Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
- 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0628850bc819080a26c5e05a1d29f |
completed | March 22, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f52829c81909bdd422cbb1eabf4 |
completed | March 23, 2026, 4:50 p.m. |
Created at: March 22, 2026, 4:21 p.m.