Triple
T2566970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katsura River |
E57373
|
entity |
| Predicate | touristAttractionIn |
P7335
|
FINISHED |
| Object | Arashiyama |
E284987
|
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: Arashiyama | Statement: [Katsura River, touristAttractionIn, Arashiyama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arashiyama Context triple: [Katsura River, touristAttractionIn, Arashiyama]
-
A.
Arashiyama district
chosen
Arashiyama district is a scenic area on the western outskirts of Kyoto, Japan, famed for its bamboo groves, historic temples, and iconic Togetsukyo Bridge.
-
B.
Fushimi
Fushimi is a historic district in Kyoto, Japan, known for its castle and its association with key events and figures of the late Sengoku period.
-
C.
Midorigaoka
Midorigaoka is a residential neighborhood located within Tokyo's Meguro ward in Japan.
-
D.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
E.
Arashiyama Bamboo Grove
Arashiyama Bamboo Grove is a famous scenic bamboo forest in Kyoto, Japan, known for its towering bamboo stalks and tranquil walking paths.
- 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_69ab4a4ef9008190a0e6d4422b9418b7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3602ed08190aad0f9c7ac577eb0 |
completed | March 7, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb66d08008190a319b19b56b6ea0d |
completed | March 10, 2026, 6:13 a.m. |
Created at: March 6, 2026, 9:48 p.m.