Triple
T14784447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wasabi |
E347476
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Wasabi-No-Ginger |
E347476
|
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: Wasabi-No-Ginger | Statement: [Wasabi, basedOn, Wasabi-No-Ginger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wasabi-No-Ginger Context triple: [Wasabi, basedOn, Wasabi-No-Ginger]
-
A.
Wasabi
chosen
Wasabi is a cautious yet skilled member of the superhero team in Disney's animated film "Big Hero 6," known for his precision, laser-based weaponry, and rule-abiding personality.
-
B.
Bawangaja
Bawangaja is a prominent Jain pilgrimage site in Madhya Pradesh, India, renowned for its towering monolithic statue of Lord Adinath carved into a hillside.
-
C.
Dashi
Dashi is a tech-savvy dachshund and the Octonauts’ photographer and IT expert who manages their communications and data.
-
D.
Dashi
Dashi is a metro station on Guangzhou's Line 7 serving the Panyu District area of Guangzhou, China.
-
E.
Ginger
Ginger is the brave and resourceful lead hen from the animated film "Chicken Run," known for masterminding escape plans from the farm.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9f1c9c8190a8b28ba0ddd3e2e3 |
completed | April 14, 2026, 11:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe24b816388190be1127fe34a58d1d |
completed | May 8, 2026, 6 p.m. |
Created at: April 10, 2026, 1:31 a.m.