Triple
T11588015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meiji-dori |
E274802
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Kiba |
E817388
|
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: Kiba | Statement: [Meiji-dori, passesThrough, Kiba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiba Context triple: [Meiji-dori, passesThrough, Kiba]
-
A.
Kiba
chosen
Kiba is a district in Tokyo’s Kōtō ward known for its history as a lumberyard area and its large urban green space, Kiba Park.
-
B.
Ryūō
Ryūō is a town in Shiga Prefecture, Japan, known for its location near Lake Biwa and its blend of rural landscapes with growing commercial development.
-
C.
Shinya
Shinya is a Japanese given name commonly used for males.
-
D.
Takehiro
Takehiro is a central character in Ryūnosuke Akutagawa’s short story “In a Grove,” whose ambiguous fate is revealed through conflicting eyewitness testimonies.
-
E.
Kojiro
Kojiro is a masculine Japanese given name that can be written with various kanji and is borne by multiple real and fictional figures.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d89463360c8190b91228c46bfe2e5f |
completed | April 10, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e7143ee0548190b71ddae0ab7c68cb |
completed | April 21, 2026, 6:07 a.m. |
Created at: April 8, 2026, 9:38 p.m.