Triple
T16069378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Kong Escapes |
E389817
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Tokyo |
E5560
|
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: Tokyo | Statement: [King Kong Escapes, setting, Tokyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokyo Context triple: [King Kong Escapes, setting, Tokyo]
-
A.
Tokyo
chosen
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
-
B.
Tokyo
"Tokyo" is a popular Afrobeats song by Ghanaian singer King Promise featuring Nigerian artist Wizkid.
-
C.
Tōkyō-wan
Tōkyō-wan is the Japanese name for Tokyo Bay, a major urban bay on the Pacific coast of Honshu that serves as a key economic and transportation hub for the Greater Tokyo Area.
-
D.
Yokohama
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
-
E.
Ome, Tokyo
Ome, Tokyo is a suburban city in western Tokyo Metropolis known for its riverside scenery, access to the Okutama mountains, and blend of residential areas with natural landscapes.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183bb98c88190ae4b5773358078be |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0167369d9481909015c34d475fac14 |
completed | May 11, 2026, 5:20 a.m. |
Created at: April 10, 2026, 4:57 a.m.