Triple
T5950603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Subcontract Act of Japan |
E132385
|
entity |
| Predicate | appliesInSector |
P20326
|
FINISHED |
| Object | manufacturing subcontracting |
—
|
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: manufacturing subcontracting | Statement: [Subcontract Act of Japan, appliesInSector, manufacturing subcontracting]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesInSector Context triple: [Subcontract Act of Japan, appliesInSector, manufacturing subcontracting]
-
A.
appliesToSectionOf
Indicates that something is relevant or applicable specifically to a particular section or subsection of a larger whole.
-
B.
isSectorSpecific
chosen
Indicates that something is tailored or restricted to a particular industry or sector rather than being generally applicable.
-
C.
operatesInSegment
Indicates that an entity conducts its activities or provides its services within a specified market or operational segment.
-
D.
appliesAt
Indicates that an action, rule, or condition is relevant to or in effect at a specific location, context, or point in time.
-
E.
appliesTo
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
- 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_69c0086b05cc8190a8f36a96927a525c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03ee10b308190afe38b904ae7c5f7 |
completed | March 22, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69c0335806788190b6488ca8b73f7a63 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:02 p.m.