Triple
T37963781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yokkaichi-juku |
E947079
|
entity |
| Predicate | neighboringPostTownPrevious |
P189202
|
FINISHED |
| Object |
Ishiyakushi-juku
Ishiyakushi-juku was a post station on Japan’s historic Tōkaidō road, serving as a lodging and transport hub between major cities during the Edo period.
|
E2250160
|
NE FINISHED |
How this triple was built (3 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: Ishiyakushi-juku | Statement: [Yokkaichi-juku, neighboringPostTownPrevious, Ishiyakushi-juku]
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: Ishiyakushi-juku Triple: [Yokkaichi-juku, neighboringPostTownPrevious, Ishiyakushi-juku]
Generated description
Ishiyakushi-juku was a post station on Japan’s historic Tōkaidō road, serving as a lodging and transport hub between major cities during the Edo period.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighboringPostTownPrevious Context triple: [Yokkaichi-juku, neighboringPostTownPrevious, Ishiyakushi-juku]
-
A.
neighboringTownship
Indicates that two townships share a common boundary and are directly adjacent to each other.
-
B.
neighboringPolis
Indicates that two city-states (poleis) are geographically adjacent or share a common boundary.
-
C.
postTown
Indicates the town or locality that serves as the postal address destination for a given address or location.
-
D.
neighboringPostTownOnTōkaidō
chosen
Indicates that two post towns are directly adjacent to each other along the historical Tōkaidō route.
-
E.
neighbouringMunicipality
Indicates that one municipality directly borders and is adjacent to another municipality.
- F. None of above.
Provenance (6 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_69f76ef7062c819091bfacb7e83aa1e0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4118088ad8819082471e0d1a0b7de2 |
completed | June 28, 2026, 12:48 p.m. |
| NEDg | Description generation | batch_6a4118c6ace88190924cd9c2f25fe982 |
completed | June 28, 2026, 12:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a411acdf2088190a22120aad1f664c5 |
completed | June 28, 2026, 12:59 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: May 3, 2026, 4:20 p.m.