Triple
T214677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Populous |
E4792
|
entity |
| Predicate | hasOfficeIn |
P1268
|
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: [Populous, hasOfficeIn, Tokyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokyo Context triple: [Populous, hasOfficeIn, Tokyo]
-
A.
Tokyo
chosen
Tokyo is Japan’s largest metropolis and a global center of finance, culture, technology, and transportation.
-
B.
Yokohama
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
-
C.
Kyoto
Kyoto is a historic Japanese city renowned for its well-preserved temples, traditional wooden houses, and role as the former imperial capital.
-
D.
Nagoya
Nagoya is a major industrial and commercial city in central Japan, known as a manufacturing hub and the capital of Aichi Prefecture.
-
E.
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.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c4ca0c8819093f63c6371e2d140 |
completed | Feb. 28, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a49eb45f188190a6e1b6d341b29f2a |
completed | March 1, 2026, 8:16 p.m. |
Created at: Feb. 28, 2026, 2:52 a.m.