Triple
T15215478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Osaka Prefecture |
E363626
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Izumi |
E8408
|
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: Izumi | Statement: [Southern Osaka Prefecture, hasMajorCity, Izumi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Izumi Context triple: [Southern Osaka Prefecture, hasMajorCity, Izumi]
-
A.
Izumi
chosen
Izumi is a city located in Osaka Prefecture, Japan, known as a residential and commercial hub in the Kansai region.
-
B.
Chikuma
Chikuma was a Japanese Imperial Navy heavy cruiser that served prominently in World War II, including major Pacific naval battles.
-
C.
Kamogawa
Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
-
D.
Kamogawa
Kamogawa is a prominent river running through Kyoto, Japan, known for its scenic banks, cultural significance, and popular walking paths.
-
E.
Ayagawa
Ayagawa is a small town in Kagawa Prefecture on Japan’s Shikoku island, known for its rural landscapes and traditional agricultural character.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3649d1408190a4fed26539de1849 |
completed | May 9, 2026, 1:27 p.m. |
Created at: April 10, 2026, 3:11 a.m.