Triple
T18473506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Higashi-Ueno Station |
E451364
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Higashi-Ueno |
—
|
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: Higashi-Ueno | Statement: [Higashi-Ueno Station, locatedIn, Higashi-Ueno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Higashi-Ueno Context triple: [Higashi-Ueno Station, locatedIn, Higashi-Ueno]
-
A.
Hamamatsucho
Hamamatsucho is a central Tokyo district and major transportation hub known for its JR and monorail stations providing access to Haneda Airport and nearby business and waterfront areas.
-
B.
Kanda-Jimbocho
Kanda-Jimbocho is Tokyo’s famed book district, renowned for its dense concentration of secondhand bookstores, publishing houses, and literary culture.
-
C.
Ueno
chosen
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
D.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
-
E.
Ueno district
Ueno district is a cultural and historical area in Tokyo known for its major museums, temples, and the expansive Ueno Park.
- 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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e530617e48819091240d4405e53aaa |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 11:34 a.m.