Triple
T21756047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnny Callison |
E537041
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Johnny |
—
|
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: Johnny | Statement: [Johnny Callison, nickname, Johnny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johnny Context triple: [Johnny Callison, nickname, Johnny]
-
A.
Johnny
chosen
Johnny is a common English masculine given name, often used as a familiar or diminutive form of John.
-
B.
Johnny
Johnny is the abrasive, intellectually sharp yet self-destructive drifter who serves as the central antihero of Mike Leigh’s 1993 British film "Naked."
-
C.
Jerry
Jerry is the given name of Jerry Lee Lewis, the influential American rock and roll and country music singer and pianist known for hits like "Great Balls of Fire."
-
D.
Jerry
Jerry is a masculine given name commonly used in English-speaking countries, often as a diminutive of names like Gerald, Jerome, or Jeremy.
-
E.
Jerry
Jerry is the video store clerk protagonist of the comedy film "Be Kind Rewind," known for recreating erased movies with homemade, low-budget remakes.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8ea04c8190a8c4fa43b3f23935 |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 16, 2026, 6:50 p.m.