Triple
T20452664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie Heaton |
E501694
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Charlie |
—
|
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: Charlie | Statement: [Charlie Heaton, givenName, Charlie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlie Context triple: [Charlie Heaton, givenName, Charlie]
-
A.
Charlie
Charlie is a central character in the romantic comedy film "French Kiss," serving as the unfaithful fiancé whose actions set the story’s events in motion.
-
B.
Charlie
chosen
Charlie is the given name of English actor and producer Charlie Bewley, known for his role as Demetri in the Twilight film series.
-
C.
Charlie
Charlie is a fictional character from Stephen Adly Guirgis’s gritty stage play "In Arabia We’d All Be Kings," which portrays the lives of struggling New Yorkers in a rapidly gentrifying Hell’s Kitchen.
-
D.
Charlie
Charlie is a classic Revlon perfume line known for its accessible, youthful, and independent image, especially popular from the 1970s onward.
-
E.
Charlie
Charlie is the ambitious New York City hustler and small-time crook who serves as the central protagonist in the crime drama film "The Pope of Greenwich Village."
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68d039af08190827bf765b50515a8 |
completed | April 20, 2026, 8:30 p.m. |
Created at: April 16, 2026, 11:32 a.m.