Triple
T21301593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tagawa |
E525077
|
entity |
| Predicate | hasOfficialLanguageDeFacto |
P237
|
FINISHED |
| Object | Japanese |
—
|
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: Japanese | Statement: [Tagawa, hasOfficialLanguageDeFacto, Japanese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Japanese Context triple: [Tagawa, hasOfficialLanguageDeFacto, Japanese]
-
A.
Japanese
chosen
Japanese is the national language of Japan, a Japonic language known for its complex writing system combining kanji and kana.
-
B.
Yapese
Yapese is an Austronesian language spoken primarily on the island of Yap and nearby islands in the western Pacific.
-
C.
JPN
JPN is the official FIFA trigramme used to represent the Japan women's national football team in international competitions and records.
-
D.
Japanese cuisine
Japanese cuisine is a culinary tradition known for its emphasis on seasonal ingredients, refined presentation, and dishes such as sushi, tempura, ramen, and kaiseki.
-
E.
JP
JP is the official Indian Railways station code for Jaipur Junction, a major railway hub in the city of Jaipur, Rajasthan.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385cd6308190bf300494833b048f |
completed | April 21, 2026, 8:42 a.m. |
Created at: April 16, 2026, 4:05 p.m.