Triple
T39892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Commons of the United Kingdom |
E788
|
entity |
| Predicate | hasSeatingArrangement |
P2362
|
FINISHED |
| Object | opposing benches |
—
|
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: opposing benches | Statement: [House of Commons of the United Kingdom, hasSeatingArrangement, opposing benches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatingArrangement Context triple: [House of Commons of the United Kingdom, hasSeatingArrangement, opposing benches]
-
A.
familySeat
Indicates the traditional principal residence or ancestral home associated with a particular family or lineage.
-
B.
arrangement
chosen
Indicates a relationship where entities are organized, ordered, or positioned in a particular configuration or sequence relative to one another.
-
C.
hasRestrooms
Indicates that a place or facility provides access to restroom or toilet amenities.
-
D.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
E.
crewCountApproximate
Indicates that the relationship specifies an estimated or approximate number of crew members associated with an entity.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b80f4a8819090d2bffe29824b90 |
completed | Feb. 28, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69a24ab74c548190a54872e15c8394c3 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.