Triple
T3990781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Court of International Trade |
E86983
|
entity |
| Predicate | canSit |
P36258
|
FINISHED |
| Object | any judicial district in the United States |
—
|
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: any judicial district in the United States | Statement: [United States Court of International Trade, canSit, any judicial district in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canSit Context triple: [United States Court of International Trade, canSit, any judicial district in the United States]
-
A.
canSitIn
chosen
Indicates that one entity is able or permitted to sit inside or occupy the seating space of another entity.
-
B.
canChair
Indicates that an entity has the authority or capability to preside over, lead, or chair a meeting, committee, or similar group.
-
C.
maySitWith
Indicates that one entity is permitted or allowed to sit together with another entity.
-
D.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
E.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb81040481909b22e4c445ecae0f |
completed | March 9, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69aef8f692008190bf4d637ffc3d3eaa |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.