Triple
T318396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara |
E7758
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Barbie |
E25542
|
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: Barbie | Statement: [Barbara, hasVariant, Barbie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barbie Context triple: [Barbara, hasVariant, Barbie]
-
A.
Barbie
chosen
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.
-
B.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
C.
Lucille
"Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
-
D.
Marilyn
A Marilyn is a type of British hill or mountain classified by having a prominence of at least 150 meters, regardless of its absolute height.
-
E.
Princess Kiko
Princess Kiko is a member of Japan’s imperial family, known as the wife of Crown Prince Fumihito and mother of Prince Hisahito, the only male heir of his generation.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea67b7588190be394a56498758b6 |
completed | Feb. 28, 2026, 1:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3c8ba3304819099db7b60f2c83c8b |
completed | March 1, 2026, 5:03 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.