Triple
T6376163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prissy |
E143469
|
entity |
| Predicate | spellingVariantOf |
P457
|
FINISHED |
| Object | Prissie |
E143469
|
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: Prissie | Statement: [Prissy, spellingVariantOf, Prissie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prissie Context triple: [Prissy, spellingVariantOf, Prissie]
-
A.
Patsy
Patsy is a given name commonly used as a diminutive of Patrick or Patricia in English-speaking contexts.
-
B.
Prissy
chosen
Prissy is a diminutive nickname for the given name Priscilla, often used as an affectionate or informal form.
-
C.
Cherie
Cherie is the naive yet determined young woman who becomes the romantic focus of the cowboy in the classic stage play and film "Bus Stop."
-
D.
Miss Prissy
Miss Prissy is a shy, spinster hen from the Looney Tunes cartoons, best known for her bonnet, spectacles, and recurring appearances alongside Foghorn Leghorn.
-
E.
Queenie
Queenie is a warm-hearted, humorous African American cook and supporting character in the classic musical "Show Boat," often providing both comic relief and emotional grounding to the story.
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0683bfc7081908b15c3c9a3c72e7b |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d9dd9dc8190b2aca25feda3e690 |
completed | March 27, 2026, 7:11 a.m. |
Created at: March 22, 2026, 4:33 p.m.