Triple
T5752234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan Gosling as Ken |
E126879
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Ken (Barbie doll) |
E543897
|
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 (Barbie doll) | Statement: [Ryan Gosling as Ken, basedOn, Ken (Barbie doll)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken (Barbie doll) Context triple: [Ryan Gosling as Ken, basedOn, Ken (Barbie doll)]
-
A.
Ken (in Barbie)
chosen
Ken (in Barbie) is a charismatic, competitive version of Barbie’s male counterpart as portrayed by Simu Liu in the 2023 live-action film.
-
B.
Barbie
Barbie is a 2023 fantasy-comedy film directed by Greta Gerwig that reimagines the iconic Mattel doll in a satirical, self-aware story exploring gender roles, identity, and consumer culture.
-
C.
Dinah Doll
Dinah Doll is a character from the children's franchise "Noddy," known as one of Noddy's close friends in Toyland.
-
D.
Barbara
Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0288b580c81909e1289982b106695 |
completed | March 22, 2026, 5:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a167f2508190a8dd507f237e771b |
completed | March 23, 2026, 2:11 a.m. |
Created at: March 22, 2026, 3:48 p.m.