Triple
T3639892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Junichiro |
E77160
|
entity |
| Predicate | hasTypicalUsage |
P37480
|
FINISHED |
| Object | personal name |
—
|
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: personal name | Statement: [Junichiro, hasTypicalUsage, personal name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalUsage Context triple: [Junichiro, hasTypicalUsage, personal name]
-
A.
hasTypicalUseContext
chosen
Indicates that something is commonly or characteristically used within a particular situation, setting, or context.
-
B.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
C.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
D.
usesStandard
Indicates that one entity adopts, follows, or operates according to a specified standard defined by another entity or reference.
-
E.
hasUsageNote
Indicates that there is an associated explanatory note describing how or when something should be used.
- F. None of above.
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_69ad85dd0be48190b738990cb20c4731 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc32b83188190bfc0ed4dc8f66730 |
completed | March 8, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69adb842be7c8190b7dfdb7c906f294c |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.