Triple
T13090843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzanne Pleshette |
E310457
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Suzanne |
E655678
|
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: Suzanne | Statement: [Suzanne Pleshette, givenName, Suzanne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suzanne Context triple: [Suzanne Pleshette, givenName, Suzanne]
-
A.
Suzanne
"Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
-
B.
Suzanne
Suzanne is a central character in Steve Martin’s play "Picasso at the Lapin Agile," representing a young woman entangled romantically with both Picasso and other men in the bohemian Parisian setting.
-
C.
Suzanne
chosen
Suzanne is a feminine given name of French origin, derived from the Hebrew name Shoshannah meaning “lily.”
-
D.
Suzie
Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
-
E.
Susie
Susie is a person whose full name is Susie Fairfield Dryden.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813acbac8190b2fe5e07287457cf |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a227d2c81908da0089d0e0387c6 |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:03 p.m.