Triple
T601933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hiroshima High Court |
E11511
|
entity |
| Predicate | numberOfInstances |
P17874
|
FINISHED |
| Object | second instance court |
—
|
LITERAL 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: second instance court | Statement: [Hiroshima High Court, numberOfInstances, second instance court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInstances Context triple: [Hiroshima High Court, numberOfInstances, second instance court]
-
A.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
B.
numberOfSites
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
C.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
D.
numberOfGroups
Indicates the total count of distinct groups associated with or formed within a given context or entity.
-
E.
numberOfIndicators
Indicates the total count of indicators associated with or relevant to a given entity or context.
- F. None of above. chosen
Provenance (4 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_69a4932779b881908688590d59c71900 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e574444819087999404f3e3ffd9 |
completed | March 1, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69a49cf701e08190966d06b9ff4b582b |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49e55b2248190b0ae3d692c0fa143 |
completed | March 1, 2026, 8:15 p.m. |
Created at: March 1, 2026, 7:35 p.m.