Triple
T4825254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakae |
E107807
|
entity |
| Predicate | touristAttractionIn |
P7335
|
FINISHED |
| Object | Nagoya |
E11598
|
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: Nagoya | Statement: [Sakae, touristAttractionIn, Nagoya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagoya Context triple: [Sakae, touristAttractionIn, Nagoya]
-
A.
Nagoya
chosen
Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
-
B.
Yokohama
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
-
C.
Osaka
Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
-
D.
Yokkaichi
Yokkaichi is an industrial port city in central Japan known for its petrochemical complexes and role as a major manufacturing hub.
-
E.
Kitakyushu
Kitakyushu is a major industrial and port city located in Fukuoka Prefecture on Japan’s Kyushu island.
- 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_69bd43fac8188190803f0327190621e4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6cadb2bc81909455149e46eb593a |
completed | March 20, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfcb931f908190b72d825b95ae4861 |
completed | March 22, 2026, 10:59 a.m. |
Created at: March 20, 2026, 1:24 p.m.