Triple
T23374272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kene Holliday |
E593562
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kene |
—
|
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: Kene | Statement: [Kene Holliday, givenName, Kene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kene Context triple: [Kene Holliday, givenName, Kene]
-
A.
Keni
chosen
Keni is a given name variant of Kenny, typically used as a personal name.
-
B.
Keiun
Keiun was a Japanese era name (nengō) from the early 8th century, used during the reign of Emperor Monmu.
-
C.
Kens
Kens are the male doll counterparts to Barbies in the fictional, pastel-colored world of Barbieland.
-
D.
Kuenn
Kuenn is a surname most notably associated with Harvey Kuenn, an American Major League Baseball player and manager.
-
E.
Neka
Neka is a city in northern Iran known for its location near the Caspian Sea and its role as an industrial and agricultural center in Mazandaran Province.
- 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_69e25d268a50819095f2fd479da8ef3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3b1d24881909945936cbf00876e |
completed | April 29, 2026, 6:22 a.m. |
Created at: April 17, 2026, 5:33 p.m.