Triple
T22567090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Cooley Bouchet |
E557979
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Susan |
—
|
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: Susan | Statement: [Susan Cooley Bouchet, givenName, Susan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susan Context triple: [Susan Cooley Bouchet, givenName, Susan]
-
A.
Susan
Susan is the birth name of American actress Sigourney Weaver, renowned for her iconic roles in science fiction and horror films such as the Alien franchise.
-
B.
Susan
chosen
Susan is a feminine given name of Hebrew origin meaning "lily" that has been widely used in English-speaking countries.
-
C.
Susan
Susan is the given name of American actress ZaSu Pitts, a prominent comedic and dramatic film star of the silent and early sound eras.
-
D.
Susan
Susan is the full given name of English actress Sue Johnston, known for her roles in British television dramas and comedies.
-
E.
Susan
Susan is a central female character in the 1971 film "Carnal Knowledge," representing one of the key romantic relationships that shape the protagonists’ emotional lives.
- 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15faaa0b081908d5aa8f3ba1e3dd3 |
completed | April 29, 2026, 1:32 a.m. |
Created at: April 16, 2026, 8:52 p.m.