Triple
T17105764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Designer Outlet |
E415094
|
entity |
| Predicate | hasLeisureUse |
P80753
|
FINISHED |
| Object | cinema |
—
|
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: cinema | Statement: [London Designer Outlet, hasLeisureUse, cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeisureUse Context triple: [London Designer Outlet, hasLeisureUse, cinema]
-
A.
hasRecreationalAspect
Indicates that something includes, involves, or is characterized by a recreational or leisure-related component or purpose.
-
B.
hasRecreationPurpose
chosen
Indicates that something is used or intended to be used for recreational or leisure activities.
-
C.
hasRecreationalUseNearby
Indicates that there is at least one location or facility for recreational activities situated close to the referenced entity.
-
D.
hasRecreationalArea
Indicates that an entity includes, provides, or is associated with a designated space intended for leisure or recreational activities.
-
E.
hasRecreationalContext
Indicates that something occurs, is used, or is understood within a leisure, entertainment, or recreational setting or purpose.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2683fc81908af2df9012addecb |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.