Triple
T16966859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nisseni |
E411561
|
entity |
| Predicate | hasAdjectiveForm |
P64799
|
FINISHED |
| Object | Nisseno |
—
|
NE NERFINISHED |
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: Nisseno | Statement: [Nisseni, hasAdjectiveForm, Nisseno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nisseno Context triple: [Nisseni, hasAdjectiveForm, Nisseno]
-
A.
Nisseni
chosen
Nisseni are the inhabitants or natives of Caltanissetta, a city in central Sicily, Italy.
-
B.
Nishio
Nishio is a city in Aichi Prefecture, Japan, known for its high-quality matcha green tea production and traditional Japanese culture.
-
C.
Sennan
Sennan is a coastal city in Osaka Prefecture, Japan, known for its proximity to Kansai International Airport and its role as part of the greater Osaka metropolitan area.
-
D.
Nikaho
Nikaho is a coastal city in northern Japan known for its scenic Sea of Japan shoreline and location in southwestern Akita Prefecture.
-
E.
Nonsan
Nonsan is a city in South Chungcheong Province, South Korea, known for its agricultural production and military training facilities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0a548b48190b87468630f3e3209 |
completed | April 18, 2026, 6:42 p.m. |
Created at: April 10, 2026, 5:31 a.m.