Triple
T511597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princeton University Press |
E10620
|
entity |
| Predicate | hasOfficeIn |
P1268
|
FINISHED |
| Object | Tokyo, Japan |
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, Japan | Statement: [Princeton University Press, hasOfficeIn, Tokyo, Japan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tokyo, Japan Context triple: [Princeton University Press, hasOfficeIn, Tokyo, Japan]
-
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.
Chiyoda, Tokyo, Japan
Chiyoda is a central special ward of Tokyo that serves as Japan’s political and administrative heart, housing the Imperial Palace, the National Diet, and many government institutions.
-
D.
Minato, Tokyo, Japan
Minato is a central special ward of Tokyo known for its major business districts, foreign embassies, and landmarks such as Tokyo Tower and Roppongi.
-
E.
Shinagawa, Tokyo, Japan
Shinagawa is a major commercial and transportation hub in southern Tokyo, known for its busy railway station, high-rise office buildings, and waterfront developments along Tokyo Bay.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f16768c081909d05537ff070868b |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6ef6be388190b4a8da5795a19aaf |
completed | March 7, 2026, 6:31 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.