Triple
T22382509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asano clan |
E553309
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Ako Domain |
—
|
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: Ako Domain | Statement: [Asano clan, associatedWith, Ako Domain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ako Domain Context triple: [Asano clan, associatedWith, Ako Domain]
-
A.
Ako
chosen
Ako is a coastal city in southwestern Hyogo Prefecture, Japan, historically known for its salt production and the story of the Forty-seven Ronin.
-
B.
Atzmi
Atzmi is a significant poetic work by Hebrew poet Uri Zvi Greenberg, reflecting his intense nationalist and existential themes.
-
C.
Acroni
Acroni is a Slovenian steel company known for producing flat-rolled steel products and supporting local sports, including ice hockey.
-
D.
Loggos
Loggos is a small, picturesque coastal village on the Greek island of Paxos, known for its harbor, traditional tavernas, and relaxed atmosphere.
-
E.
Cybinka
Cybinka is a small town in western Poland near the German border, known for its surrounding forests and proximity to the Oder River.
- 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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1582cce608190b5324b30f349a3ff |
completed | April 29, 2026, 1 a.m. |
Created at: April 16, 2026, 8:45 p.m.