Triple
T19983069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Man Up |
E493864
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Jack |
—
|
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: Jack | Statement: [Man Up, mainCharacter, Jack]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Context triple: [Man Up, mainCharacter, Jack]
-
A.
Jack
Jack is the standard botanical author abbreviation for William Jack, a 19th-century Scottish physician and botanist known for his work on Southeast Asian flora.
-
B.
Jack
chosen
Jack is a common masculine given name, often used as a familiar form of John and widely featured in English-language literature and popular culture.
-
C.
Jimmy
Jimmy is a supporting character in the 1980s crime drama series "The Equalizer," appearing in stories centered on vigilante justice and urban crime.
-
D.
Jimmy
Jimmy is a character in the crime film "Hard Eight," involved in the story’s underworld gambling and con-artist schemes.
-
E.
Jimmy
Jimmy is the given name of American actor Jimmy Smits, known for his roles in television series such as "L.A. Law," "NYPD Blue," and "The West Wing."
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d14c3c08190b1ba8f4da08a4ccf |
completed | April 20, 2026, 5:06 p.m. |
Created at: April 11, 2026, 3:28 p.m.