Triple
T21715725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 高崎線 |
E536023
|
entity |
| Predicate | 所在都道府県 |
P17718
|
FINISHED |
| Object | 東京都 |
—
|
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: 東京都 | Statement: [高崎線, 所在都道府県, 東京都]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 東京都 Context triple: [高崎線, 所在都道府県, 東京都]
-
A.
Tokyo Prefecture
Tokyo Prefecture is Japan’s capital metropolitan region, encompassing the city of Tokyo and serving as the country’s political, economic, and cultural center.
-
B.
Tōkyō-wan
Tōkyō-wan is the Japanese name for Tokyo Bay, a major urban bay on the Pacific coast of Honshu that serves as a key economic and transportation hub for the Greater Tokyo Area.
-
C.
Tokyo
"Tokyo" is a popular Afrobeats song by Ghanaian singer King Promise featuring Nigerian artist Wizkid.
-
D.
Tokyo
Tokyo is a central member of the Professor's heist crew in the Spanish television series "Money Heist" ("La Casa de Papel"), known for her impulsive nature and role as one of the show's primary narrators.
-
E.
Tokyo
chosen
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efb53761b48190954a46e8155a84f0 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 16, 2026, 6:47 p.m.