Triple
T6734759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viscount Althorp |
E153724
|
entity |
| Predicate | notAPeerageSeat |
P72645
|
FINISHED |
| Object | does not confer a seat in the House of Lords |
—
|
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: does not confer a seat in the House of Lords | Statement: [Viscount Althorp, notAPeerageSeat, does not confer a seat in the House of Lords]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notAPeerageSeat Context triple: [Viscount Althorp, notAPeerageSeat, does not confer a seat in the House of Lords]
-
A.
otherSeat
Indicates that one entity is the alternative or different seat relative to another seat in a given context.
-
B.
typicalSeat
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.
laterSeat
Indicates that one entity is seated in a position that comes after another entity in a specified ordering or sequence of seats.
-
E.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
- F. None of above. chosen
Provenance (4 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16ecbe08190b019d547f631a725 |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d09067a0819087ed6c820f4699f8 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d14e18d481908aaac34897c650f1 |
completed | March 27, 2026, 6:49 p.m. |
Created at: March 27, 2026, 2:09 p.m.