Triple
T1267151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Convention for the Protection of Industrial Property |
E15626
|
entity |
| Predicate | coversSubjectMatter |
P450
|
FINISHED |
| Object | patents |
—
|
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: patents | Statement: [Paris Convention for the Protection of Industrial Property, coversSubjectMatter, patents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversSubjectMatter Context triple: [Paris Convention for the Protection of Industrial Property, coversSubjectMatter, patents]
-
A.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
B.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
C.
isSubjectTo
Indicates that one entity is governed, affected, or constrained by the authority, rules, conditions, or influence of another entity.
-
D.
usesResearchSubject
Indicates that one entity employs or utilizes another entity as a research subject in a study or investigation.
-
E.
subjectCanBe
Indicates that the subject has the potential or capability to assume, become, or be classified as the specified object or state.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c037f14c8190baa42f70f8846583 |
completed | March 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.