Triple
T17911659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Squidbillies |
E447830
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Early Cuyler |
—
|
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: Early Cuyler | Statement: [Squidbillies, mainCharacter, Early Cuyler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Early Cuyler Context triple: [Squidbillies, mainCharacter, Early Cuyler]
-
A.
Cuyler
chosen
Cuyler is a small unincorporated community located in rural Baker County in the U.S. state of Florida.
-
B.
Conerly
Conerly is a surname most notably associated with Charlie Conerly, a prominent mid-20th-century American football quarterback.
-
C.
McCauley
McCauley is the maiden surname of Rosa Parks, the prominent American civil rights activist known for her pivotal role in the Montgomery bus boycott.
-
D.
Cates
Cates is a surname most notably associated with American actress Darlene Cates, known for her role in the film "What's Eating Gilbert Grape."
-
E.
McClurg
McClurg is the namesake of the historic McClurg Building, a notable structure recognized for its architectural and cultural significance.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49ea0ea008190b54a999e0704fb67 |
completed | April 19, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:19 a.m.