Triple
T2989257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Dining Room (10 Downing Street) |
E80705
|
entity |
| Predicate | hasTableType |
P11780
|
FINISHED |
| Object | long formal dining table |
—
|
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: long formal dining table | Statement: [State Dining Room (10 Downing Street), hasTableType, long formal dining table]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTableType Context triple: [State Dining Room (10 Downing Street), hasTableType, long formal dining table]
-
A.
hasTableConfiguration
chosen
Indicates that an entity is associated with or defined by a specific arrangement or setup of a table and its properties.
-
B.
hasTableService
Indicates that a place or establishment provides table service, where staff serve customers at their tables rather than requiring self-service.
-
C.
hasPropertyType
Indicates that an entity possesses or is associated with a specific type or category of property.
-
D.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
-
E.
hasMapType
Indicates that one entity is associated with a specific type or category of map.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99dcdb00819092ca5f10396408e0 |
completed | March 8, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ad961403108190bbecb8d3608fd4e0 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.