Triple
T23465808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charl Schwartzel |
E569099
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Charl |
—
|
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: Charl | Statement: [Charl Schwartzel, givenName, Charl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charl Context triple: [Charl Schwartzel, givenName, Charl]
-
A.
Charl
chosen
Charl is a given name, typically a shortened or variant form of the name Charles.
-
B.
Cha
Cha is the Korean family name of Theresa Hak Kyung Cha, the avant-garde artist and writer best known for her experimental book "Dictee."
-
C.
Mr. Charlie
"Mr. Charlie" is a bluesy rock song by the Grateful Dead, known from their early-1970s live repertoire and featured on the live album Europe '72.
-
D.
Charlie
Charlie is a character featured in the work titled "Seascape."
-
E.
Charlie
Charlie is the reclusive, morbidly obese English professor at the center of Darren Aronofsky’s film "The Whale," whose struggle with grief, guilt, and self-destruction drives the story’s emotional core.
- 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_69e2458ebd808190b3298163132cfb0b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a6faaf8c8190b4fd191c54e1acea |
completed | April 29, 2026, 6:36 a.m. |
Created at: April 17, 2026, 5:54 p.m.