Triple
T720445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Court of International Trade |
E14603
|
entity |
| Predicate | handlesCases |
P16126
|
FINISHED |
| Object | civil cases only |
—
|
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: civil cases only | Statement: [United States Court of International Trade, handlesCases, civil cases only]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: handlesCases Context triple: [United States Court of International Trade, handlesCases, civil cases only]
-
A.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
B.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
C.
hearsCasesUnder
chosen
Indicates that a judicial body or authority has the responsibility and power to adjudicate legal cases falling within a specified jurisdiction, category, or scope.
-
D.
hasTypeOfCase
Indicates that an entity is associated with or classified under a particular type or category of case.
-
E.
oneOfCasesIn
Indicates that an entity is one specific member of a defined set or collection of possible cases.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58e65e8819098cba7e6a20d8f33 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f513608190b716b939d574c292 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.