Triple
T5741132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian-American Chamber of Commerce USA |
E126615
|
entity |
| Predicate | hasBinationalCharacter |
P26170
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Norwegian-American Chamber of Commerce USA, hasBinationalCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBinationalCharacter Context triple: [Norwegian-American Chamber of Commerce USA, hasBinationalCharacter, true]
-
A.
isBinational
chosen
Indicates that an entity is associated with or recognized by two distinct nations, such as holding dual nationality or operating under the authority of two countries.
-
B.
isBinationalComponentOf
Indicates that an entity is a component or part of a larger structure, project, or system that is jointly established, managed, or recognized by two nations.
-
C.
hasDualCitizenship
Indicates that a person is legally recognized as a citizen of two different countries at the same time.
-
D.
isBilateralMissionBetween
Indicates a mission or undertaking that is jointly conducted between exactly two parties or entities, reflecting a mutual or reciprocal relationship.
-
E.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
- 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0258382908190af8787feb1e5fbcd |
completed | March 22, 2026, 5:23 p.m. |
| PD | Predicate disambiguation | batch_69c021c8195481909419808b002628aa |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:48 p.m.