Triple
T23077928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raymond Blanco |
E575381
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Kathleen Blanco |
—
|
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: Kathleen Blanco | Statement: [Raymond Blanco, spouse, Kathleen Blanco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kathleen Blanco Context triple: [Raymond Blanco, spouse, Kathleen Blanco]
-
A.
Kathleen Blanco
chosen
Kathleen Blanco was an American Democratic politician who served as the first female governor of Louisiana, leading the state during the devastation of Hurricane Katrina.
-
B.
Kathleen Avila
Kathleen Avila is a fictional character appearing in the crime drama film "Internal Affairs."
-
C.
Kathy Saavedra
Kathy Saavedra is a screenwriter best known for her work on the film "Un lugar en el mundo."
-
D.
Paula Cancio
Paula Cancio is a Spanish actress known for her work in film and television, including a prominent role in the comedy-drama "Felices 140."
-
E.
Lisa Molina
Lisa Molina is a character associated with the musical project Dream, likely serving as a vocalist or prominent contributor within the act.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c6455f48190b84eaecdead0d963 |
completed | April 29, 2026, 4:43 a.m. |
Created at: April 17, 2026, 3:56 p.m.