Triple
T3046309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spiral Building |
E83455
|
entity |
| Predicate | commissionedBy |
P27
|
FINISHED |
| Object | Wacoal |
E323147
|
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: Wacoal | Statement: [Spiral Building, commissionedBy, Wacoal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wacoal Context triple: [Spiral Building, commissionedBy, Wacoal]
-
A.
Wacoal
chosen
Wacoal is a Japanese company best known as a leading manufacturer and retailer of women's lingerie and intimate apparel.
-
B.
Isabel Jeans
Isabel Jeans was a British stage and film actress known for her sophisticated roles in early 20th-century cinema, including appearances in several Alfred Hitchcock films.
-
C.
Bata
Bata is a major port city on the mainland of Equatorial Guinea, serving as a key economic and transportation hub for the country.
-
D.
Semler
Semler is a German surname most notably associated with Johann Salomo Semler, an influential 18th-century biblical scholar and theologian.
-
E.
Ursula Jeans
Ursula Jeans was a British stage and film actress known for her versatile character roles in mid-20th-century cinema and theatre.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bab541c8190a17aca26b3dcfae7 |
completed | March 8, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f86f538481908e3e9c286e4fb676 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:01 p.m.