Triple
T7052463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Supreme Court building, London |
E163998
|
entity |
| Predicate | hasCourtroomCount |
P57222
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Supreme Court building, London, hasCourtroomCount, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCourtroomCount Context triple: [Supreme Court building, London, hasCourtroomCount, 3]
-
A.
numberOfCourtrooms
chosen
Indicates the total count of courtrooms associated with a given legal facility, jurisdiction, or court entity.
-
B.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
C.
hasCourtroomScenes
Indicates that the work contains one or more scenes set in a courtroom or depicting courtroom proceedings.
-
D.
hasCourts
Indicates that an entity possesses, contains, or is equipped with one or more courts (e.g., legal, sports, or judicial facilities).
-
E.
hasNumberOfJurors
Indicates the relationship specifying how many jurors are associated with a given legal case, trial, or proceeding.
- 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_69c68861678881909961ddf4d779f750 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bdc1f08190975fcdbbb1854d1e |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:37 p.m.