Triple
T14900098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanan |
E359980
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Kashiwara |
E72666
|
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: Kashiwara | Statement: [Kanan, locatedNear, Kashiwara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kashiwara Context triple: [Kanan, locatedNear, Kashiwara]
-
A.
Kashiwara
chosen
Kashiwara is a city in Osaka Prefecture, Japan, known as a suburban residential and commercial area within the Kansai region’s rail network.
-
B.
Kodaira
Kodaira is a suburban city in western Tokyo, Japan, known as a residential area with parks, schools, and convenient rail access to central Tokyo.
-
C.
Kawamata
Kawamata is a small town in Fukushima Prefecture, Japan, known for its traditional silk production and rural landscapes.
-
D.
Wakamatsu
Wakamatsu is a ward in the city of Kitakyushu, Japan, known historically as a port and industrial area on the northern coast of Kyushu.
-
E.
Yamakoshi
Yamakoshi is a recurring character from the Disney XD sitcom "Pair of Kings," known as a mystical fish with prophetic abilities and a quirky, comedic presence.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded609bf68819099ca3aa3fe1acadc |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e83418081908280a9ed8ddb9fd7 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:11 a.m.