Triple
T414670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regal Entertainment Group |
E9565
|
entity |
| Predicate | numberOfScreens |
P2426
|
FINISHED |
| Object | over 7000 |
—
|
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: over 7000 | Statement: [Regal Entertainment Group, numberOfScreens, over 7000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfScreens Context triple: [Regal Entertainment Group, numberOfScreens, over 7000]
-
A.
hasNumberOfScreens
chosen
Indicates the quantity of screens associated with or contained in a given entity.
-
B.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
C.
supportsDeviceCount
Indicates the number of devices that a system, service, or component is capable of supporting concurrently.
-
D.
numberOfTerminals
Indicates the total count of terminal points or endpoints associated with an entity.
-
E.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edcff4688190809d83d112ff25a5 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.