Triple
T11458563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Thorne |
E271589
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Ken |
E795904
|
NE FINISHED |
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: Ken | Statement: [Ken Thorne, hasGivenName, Ken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken Context triple: [Ken Thorne, hasGivenName, Ken]
-
A.
Ken
Ken is the nickname of Ken Dryden, the legendary Canadian Hall of Fame goaltender best known for backstopping the Montreal Canadiens to multiple Stanley Cup championships in the 1970s.
-
B.
Ken
Ken is the iconic male doll character and Barbie’s counterpart, portrayed in the 2023 film as a comically self-aware and insecure figure exploring identity and patriarchy.
-
C.
Ken
chosen
Ken is a masculine given name commonly used in English-speaking countries, often as a short form of Kenneth.
-
D.
Ken
Ken is a character in Leslie Marmon Silko’s short story "The Man to Send Rain Clouds," which explores Native American traditions and cultural conflict.
-
E.
Kevin
Kevin is a common masculine given name of Irish origin, meaning "handsome" or "kind."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f2138081909408c7916cef99c9 |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e911c03c819081f1447b320dd2f2 |
completed | April 20, 2026, 8:51 a.m. |
Created at: April 8, 2026, 9:35 p.m.