Triple
T28136819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piccadilly Circus advertising screens |
E714229
|
entity |
| Predicate | numberOfMainScreens |
P2426
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Piccadilly Circus advertising screens, numberOfMainScreens, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainScreens Context triple: [Piccadilly Circus advertising screens, numberOfMainScreens, 1]
-
A.
hasNumberOfScreens
chosen
Indicates the quantity of screens associated with or contained in a given entity.
-
B.
numberOfWindows
Indicates the count of windows associated with a given entity.
-
C.
numberOfMainElements
Indicates the quantity of primary or central elements associated with an entity or structure.
-
D.
hasPhysicalDisplays
Indicates that an entity possesses one or more tangible, visible display units or interfaces.
-
E.
hasNumberOfMainModes
Indicates the relationship that specifies how many primary or main modes (e.g., ways of operation or types) are associated with a given 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_69efd6af156c81908f50c2cd7db0e1ef |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69ff41645c548190b7cb4e53079b93ef |
completed | May 9, 2026, 2:15 p.m. |
| PD | Predicate disambiguation | batch_69ff410aa33c8190869ba769ac2a93ce |
completed | May 9, 2026, 2:13 p.m. |
Created at: April 27, 2026, 9:50 p.m.