Triple
T1265544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 11 Downing Street |
E12591
|
entity |
| Predicate | hasDiningRoomUsedFor |
P8225
|
FINISHED |
| Object | official functions |
—
|
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: official functions | Statement: [11 Downing Street, hasDiningRoomUsedFor, official functions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiningRoomUsedFor Context triple: [11 Downing Street, hasDiningRoomUsedFor, official functions]
-
A.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
B.
hasMainHall
Indicates that an entity possesses or includes a primary or central hall as a significant internal space.
-
C.
hasHumanUse
chosen
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
D.
hasMainHallType
Indicates the specific category or kind of main hall associated with an entity.
-
E.
eatenOnOccasion
Indicates that one entity is consumed or eaten by another only at certain times or under specific circumstances, rather than regularly or habitually.
- 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4c036deb881909b234894347c75c6 |
completed | March 1, 2026, 10:39 p.m. |
| PD | Predicate disambiguation | batch_69a4bb7058e88190825b0cba5ee60b51 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:50 p.m.