Triple
T30644952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Civil Division of the Court of Appeal |
E780094
|
entity |
| Predicate | usuallySitsAt |
P48956
|
FINISHED |
| Object | Royal Courts of Justice |
—
|
NE NERFINISHED |
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: Royal Courts of Justice | Statement: [Civil Division of the Court of Appeal, usuallySitsAt, Royal Courts of Justice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usuallySitsAt Context triple: [Civil Division of the Court of Appeal, usuallySitsAt, Royal Courts of Justice]
-
A.
oftenSits
Indicates that an entity frequently assumes a sitting position, either habitually or on many occasions.
-
B.
typicalSeat
chosen
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
C.
hasSeatAt
Indicates that an entity occupies or holds a place, position, or membership within a specific group, body, or location.
-
D.
seatOftenLocatedIn
Indicates that one type of seat is commonly or typically found within a particular location or setting.
-
E.
sitterIn
Indicates that one entity is acting as a sitter (e.g., babysitter, pet sitter, house sitter) for another entity or at a particular place.
- 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_69f224a50ebc81909b961a94c7f66b12 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68a58cd808190bc1cdaa106291084 |
completed | May 2, 2026, 11:35 p.m. |
| PD | Predicate disambiguation | batch_69f67e448a9c8190b591374d98799fe3 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 8:29 p.m.