Triple
T12086904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Bus Kansai |
E287829
|
entity |
| Predicate | serviceArea |
P82
|
FINISHED |
| Object | Shiga |
E34281
|
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: Shiga | Statement: [JR Bus Kansai, serviceArea, Shiga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiga Context triple: [JR Bus Kansai, serviceArea, Shiga]
-
A.
Shiga
chosen
Shiga is a landlocked prefecture in central Japan known for encompassing Lake Biwa, the country’s largest freshwater lake, and for its historical sites and natural scenery.
-
B.
Gogawa
Gogawa is a small town located in the Khargone district of the Indian state of Madhya Pradesh.
-
C.
Ishkashimi
Ishkashimi is a lesser-known Eastern Iranian language spoken by small communities in parts of Afghanistan and Tajikistan.
-
D.
Nakagawa
Nakagawa is a river in Japan, likely a tributary or neighboring waterway associated with the Edogawa River system.
-
E.
Tatsuno
Tatsuno is a city in western Japan known for its traditional soy sauce production and historic townscape within Hyogo Prefecture.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91514c78c8190bc1cd569e524e8b4 |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6684b79c48190a663e9f5504ba20c |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:48 p.m.