Triple
T13850008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albert V. Bryan U.S. Courthouse |
E332908
|
entity |
| Predicate | hasCourtrooms |
P20573
|
FINISHED |
| Object | multiple federal 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: multiple federal courtrooms | Statement: [Albert V. Bryan U.S. Courthouse, hasCourtrooms, multiple federal courtrooms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCourtrooms Context triple: [Albert V. Bryan U.S. Courthouse, hasCourtrooms, multiple federal courtrooms]
-
A.
hasCourtroomsFor
Indicates that an entity provides or contains courtrooms designated for use by another entity or purpose.
-
B.
numberOfCourtrooms
Indicates the total count of courtrooms associated with a given legal facility, jurisdiction, or court entity.
-
C.
hasCourts
chosen
Indicates that an entity possesses, contains, or is equipped with one or more courts (e.g., legal, sports, or judicial facilities).
-
D.
hasCourtroomScenes
Indicates that the work contains one or more scenes set in a courtroom or depicting courtroom proceedings.
-
E.
hasCourtInEach
Indicates that an entity possesses or maintains a court in every member of a specified set of locations or jurisdictions.
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02d8fb788190baef7537be2baecb |
completed | April 14, 2026, 9:03 a.m. |
| PD | Predicate disambiguation | batch_69dbc8691b608190a25a7c70a366b170 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:14 p.m.