Triple
T22823269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virgil I. Grissom |
E565281
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Gus |
—
|
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: Gus | Statement: [Virgil I. Grissom, hasNickname, Gus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gus Context triple: [Virgil I. Grissom, hasNickname, Gus]
-
A.
Gus
Gus is the given name of American filmmaker Gus Van Sant, known for directing independent and mainstream films such as "Good Will Hunting" and "Milk."
-
B.
Gus
Gus is a supporting character in the crime action film "3000 Miles to Graceland," which centers on a group of criminals executing a heist during an Elvis impersonator convention.
-
C.
Gus
Gus is the lovable, chubby mouse in Disney's 1950 animated film "Cinderella," known for his comic relief and loyal friendship to Cinderella.
-
D.
Gus
chosen
Gus is the nickname of Virgil "Gus" Grissom, one of NASA's original Mercury Seven astronauts and a pioneering American spacefarer.
-
E.
Gus
Gus is a 1976 Disney sports comedy film about a football team that gains an unlikely advantage from a field-goal-kicking mule.
- 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_69e2458426188190b58b8ab4844fe420 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17dd2005081909baef070124eb839 |
completed | April 29, 2026, 3:41 a.m. |
Created at: April 17, 2026, 3:34 p.m.