Triple
T2635791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E. Barrett Prettyman United States Courthouse |
E59741
|
entity |
| Predicate | typeOfCourtFacilities |
P25397
|
FINISHED |
| Object | courtrooms |
—
|
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: courtrooms | Statement: [E. Barrett Prettyman United States Courthouse, typeOfCourtFacilities, courtrooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfCourtFacilities Context triple: [E. Barrett Prettyman United States Courthouse, typeOfCourtFacilities, courtrooms]
-
A.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
-
B.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
C.
campusFacilityType
chosen
Indicates the specific kind of facility a campus location is classified as (e.g., library, laboratory, residence hall).
-
D.
hasCourts
Indicates that an entity possesses, contains, or is equipped with one or more courts (e.g., legal, sports, or judicial facilities).
-
E.
judicialCircuit
Indicates the judicial circuit within which a legal entity, case, or jurisdiction is organized or falls under authority.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8e1fffc81908e4921690098c8db |
completed | March 7, 2026, 7:50 a.m. |
| PD | Predicate disambiguation | batch_69abd812849881908f956845a80e0205 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:50 p.m.