Triple
T22023639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken (Barbie) |
E543901
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
BMR1959 Ken
BMR1959 Ken is a modern, fashion-forward reinterpretation of the classic Ken doll, featuring streetwear-inspired styling and diverse, contemporary aesthetics.
|
E1514267
|
NE FINISHED |
How this triple was built (4 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: BMR1959 Ken | Statement: [Ken (Barbie), hasVariant, BMR1959 Ken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BMR1959 Ken Context triple: [Ken (Barbie), hasVariant, BMR1959 Ken]
-
A.
Kawasaki More’s
Kawasaki More’s is a shopping complex located in Kawasaki-ku, Japan, offering a variety of retail stores, dining options, and services.
-
B.
BR-43
BR-43 is the vehicle registration code assigned to the Madhepura district in the Indian state of Bihar.
-
C.
Breda A650
The Breda A650 is a heavy-rail rapid transit car used by the Los Angeles Metro system, notable for serving lines such as the Red Line in its subway network.
-
D.
Bernmobil
Bernmobil is the public transport company responsible for operating trams, buses, and other urban transit services in the Swiss city of Bern.
-
E.
BR-425
BR-425 is a federal highway in Brazil that connects the municipality of Guajará-Mirim in Rondônia to other parts of the state and national road network.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: BMR1959 Ken Triple: [Ken (Barbie), hasVariant, BMR1959 Ken]
Generated description
BMR1959 Ken is a modern, fashion-forward reinterpretation of the classic Ken doll, featuring streetwear-inspired styling and diverse, contemporary aesthetics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BMR1959 Ken Target entity description: BMR1959 Ken is a modern, fashion-forward reinterpretation of the classic Ken doll, featuring streetwear-inspired styling and diverse, contemporary aesthetics.
-
A.
Kawasaki More’s
Kawasaki More’s is a shopping complex located in Kawasaki-ku, Japan, offering a variety of retail stores, dining options, and services.
-
B.
BR-43
BR-43 is the vehicle registration code assigned to the Madhepura district in the Indian state of Bihar.
-
C.
Breda A650
The Breda A650 is a heavy-rail rapid transit car used by the Los Angeles Metro system, notable for serving lines such as the Red Line in its subway network.
-
D.
Bernmobil
Bernmobil is the public transport company responsible for operating trams, buses, and other urban transit services in the Swiss city of Bern.
-
E.
BR-425
BR-425 is a federal highway in Brazil that connects the municipality of Guajará-Mirim in Rondônia to other parts of the state and national road network.
- F. None of above. chosen
Provenance (5 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127c9959481908da6bed356199f75 |
completed | April 28, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a738722a88190898c5fc5f37e5b5d |
completed | May 18, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_6a0a745e534881909a681b9f2234afa5 |
completed | May 18, 2026, 2:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a7857e6dc819088b592cdff7d4cf3 |
completed | May 18, 2026, 2:24 a.m. |
Created at: April 16, 2026, 8:23 p.m.