Triple
T8061497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wako |
E188130
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Shiki
Shiki is a city in Saitama Prefecture, Japan, known as a residential and commercial hub within the Greater Tokyo metropolitan area.
|
E717837
|
NE FINISHED |
How this triple was built (4 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: Shiki | Statement: [Wako, borderedBy, Shiki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiki Context triple: [Wako, borderedBy, Shiki]
-
A.
Aishō
Aishō is a town in Shiga Prefecture, Japan, known for its rural character and historical sites.
-
B.
Shinsekai
Shinsekai is a retro entertainment district in Osaka, Japan, known for its nostalgic Showa-era atmosphere, street food, and neon-lit nightlife.
-
C.
Katsuragi
Katsuragi was a late-war Imperial Japanese Navy aircraft carrier that served in the Pacific Theater during World War II.
-
D.
Katsuragi
Katsuragi is a city in Japan known for its location in Nara Prefecture and its historical and cultural ties to the ancient Yamato region.
-
E.
Hozuki-ichi
Hozuki-ichi is a traditional summer fair in Asakusa, Tokyo, known for its stalls selling bright orange hōzuki (ground cherry) plants and its association with visits to Senso-ji Temple.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shiki Triple: [Wako, borderedBy, Shiki]
Generated description
Shiki is a city in Saitama Prefecture, Japan, known as a residential and commercial hub within the Greater Tokyo metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shiki Target entity description: Shiki is a city in Saitama Prefecture, Japan, known as a residential and commercial hub within the Greater Tokyo metropolitan area.
-
A.
Aishō
Aishō is a town in Shiga Prefecture, Japan, known for its rural character and historical sites.
-
B.
Shinsekai
Shinsekai is a retro entertainment district in Osaka, Japan, known for its nostalgic Showa-era atmosphere, street food, and neon-lit nightlife.
-
C.
Katsuragi
Katsuragi was a late-war Imperial Japanese Navy aircraft carrier that served in the Pacific Theater during World War II.
-
D.
Katsuragi
Katsuragi is a city in Japan known for its location in Nara Prefecture and its historical and cultural ties to the ancient Yamato region.
-
E.
Hozuki-ichi
Hozuki-ichi is a traditional summer fair in Asakusa, Tokyo, known for its stalls selling bright orange hōzuki (ground cherry) plants and its association with visits to Senso-ji Temple.
- F. None of above. chosen
Provenance (5 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_69ca82b2f68881908c50560697e210da |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3fcc61c0819085edc26e75c5f6d5 |
completed | March 31, 2026, 3:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccecdfddb08190bfda3bb5c02215d9 |
completed | April 1, 2026, 10:01 a.m. |
| NEDg | Description generation | batch_69ccf0982f4481908e2a59424fdf470f |
completed | April 1, 2026, 10:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd051913708190a83f925cf0cbbaa1 |
completed | April 1, 2026, 11:44 a.m. |
Created at: March 30, 2026, 5:26 p.m.