Triple
T940757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese occupation of Singapore |
E20299
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Syonan |
E110623
|
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: Syonan | Statement: [Japanese occupation of Singapore, alsoKnownAs, Syonan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Syonan Context triple: [Japanese occupation of Singapore, alsoKnownAs, Syonan]
-
A.
Syonan-to
chosen
Syonan-to was the name given by Imperial Japan to Singapore during its World War II occupation from 1942 to 1945.
-
B.
Sion
Sion is a historic Swiss town in the canton of Valais, known for its hilltop castles, vineyards, and role as a regional cultural and administrative center.
-
C.
Tenjin
Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
-
D.
Daikanyama
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
E.
Sendagaya
Sendagaya is a neighborhood in Tokyo known for its sports facilities, including the National Stadium, and its proximity to Shinjuku and Harajuku.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b38cc6888190b1d9043ec8fbcbc3 |
completed | March 1, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a933a643708190a8d3da54b1e91bcf |
completed | March 5, 2026, 7:41 a.m. |
Created at: March 1, 2026, 7:40 p.m.