Triple
T509602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shin-Osaka Station |
E10575
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Kobe |
E3089
|
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: Kobe | Statement: [Shin-Osaka Station, connectsTo, Kobe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kobe Context triple: [Shin-Osaka Station, connectsTo, Kobe]
-
A.
Kobe
chosen
Kobe is a major port city in Japan’s Kansai region, known for its scenic harbor setting, cosmopolitan atmosphere, and famous Kobe beef.
-
B.
Hotto Motto Field Kobe
Hotto Motto Field Kobe is a baseball stadium in Kobe, Japan, known for hosting Nippon Professional Baseball games and serving as a secondary home venue for the Orix Buffaloes.
-
C.
Henderson
Henderson is a major city in the Las Vegas metropolitan area known for its rapid growth, residential communities, and proximity to the Las Vegas Strip.
-
D.
Daikanyama
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
E.
Port of Kobe
The Port of Kobe is one of Japan’s major international seaports, serving as a key hub for container shipping and maritime trade 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f164a9d48190b525a97b5c06ffe2 |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4b8a96a188190b7a6be463de3d736 |
completed | March 1, 2026, 10:07 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.