Triple
T13206886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mogami River |
E314387
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Sakata |
E1110900
|
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: Sakata | Statement: [Mogami River, flowsThrough, Sakata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakata Context triple: [Mogami River, flowsThrough, Sakata]
-
A.
Sakata
Sakata is a Japanese surname borne by various notable individuals in fields such as physics, sports, and entertainment.
-
B.
Sakata
chosen
Sakata is a coastal city in Yamagata Prefecture, Japan, known historically as a prominent port and trading center on the Sea of Japan.
-
C.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
D.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
-
E.
Wakatsuki
Wakatsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk during late-war Pacific naval operations.
- 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c9cb7ac819095cff8699993c419 |
completed | April 10, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5b543308190a86e715106641484 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 9, 2026, 9:17 p.m.