Triple
T3343943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Court of Customs and Patent Appeals |
E70324
|
entity |
| Predicate | subjectMatterSpecialization |
P466
|
FINISHED |
| Object | international trade |
—
|
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: international trade | Statement: [United States Court of Customs and Patent Appeals, subjectMatterSpecialization, international trade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectMatterSpecialization Context triple: [United States Court of Customs and Patent Appeals, subjectMatterSpecialization, international trade]
-
A.
subjectMatter
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
C.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
D.
hasSpecialty
chosen
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
E.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1f23008819084ea68b8431c50ab |
completed | March 8, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69ada42df1d48190874bb05f95deefde |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:12 p.m.