Triple
T17856797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Howard |
E445958
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Sandra Howard |
—
|
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: Sandra Howard | Statement: [Michael Howard, spouse, Sandra Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sandra Howard Context triple: [Michael Howard, spouse, Sandra Howard]
-
A.
Sandra Howard
chosen
Sandra Howard is a British former fashion model and novelist who became known as the wife of Conservative politician Michael Howard.
-
B.
Sandra Jennings
Sandra Jennings is an American woman best known for her long-term relationship and legal disputes with actor William Hurt.
-
C.
Sandra Jolley
Sandra Jolley was the wife of American businessman and quality management expert Philip Crosby.
-
D.
Sandra Nelson
Sandra Nelson is an American actress known for her roles in film and television, including a part in the Cole Porter biographical musical film "De-Lovely."
-
E.
Sandra Newman
Sandra Newman is an American novelist and writer known for her inventive, genre-blending fiction and works such as "The Country of Ice Cream Star" and "The Men."
- 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4978bd5e081909e192f6aada5235f |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 10:17 a.m.