Triple
T11491962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nishinomiya Shrine |
E272435
|
entity |
| Predicate | hasNearbyCity |
P350
|
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: [Nishinomiya Shrine, hasNearbyCity, Kobe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kobe Context triple: [Nishinomiya Shrine, hasNearbyCity, 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.
Neo Kobe City
Neo Kobe City is a futuristic, cyberpunk metropolis shrouded in perpetual rain and noir atmosphere, serving as the primary backdrop for Hideo Kojima’s adventure game Snatcher.
-
C.
Kobe Nankinmachi
Kobe Nankinmachi is a famous Chinatown district in Kobe, Japan, known for its Chinese restaurants, shops, and vibrant cultural festivals.
-
D.
Kobe – Naha
Kobe – Naha is a domestic air route in Japan connecting Kobe in Hyōgo Prefecture with Naha in Okinawa Prefecture.
-
E.
Kobe, Hyogo, Japan
Kobe, Hyogo, Japan is a major port city in western Japan known for its international trade, scenic harbor setting between mountains and sea, and famous Kobe beef.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a21fb048190acb0960aefd4fe0a |
completed | April 10, 2026, 2:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6047d95548190b445aa3b4f1e71cc |
completed | April 20, 2026, 10:48 a.m. |
Created at: April 8, 2026, 9:36 p.m.