Triple
T414681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Regal Entertainment Group |
E9565
|
entity |
| Predicate | hasApp |
P14571
|
FINISHED |
| Object | Regal mobile app |
—
|
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: Regal mobile app | Statement: [Regal Entertainment Group, hasApp, Regal mobile app]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApp Context triple: [Regal Entertainment Group, hasApp, Regal mobile app]
-
A.
hasCP
Indicates that an entity possesses, is associated with, or is characterized by a specific CP (such as a control point, contact person, or configuration parameter), depending on the domain context.
-
B.
hasPar
Indicates a relationship where one entity has another entity as its parent.
-
C.
hasProvisionOn
Indicates that one entity contains, specifies, or includes a particular provision, clause, or stipulation concerning another entity or subject.
-
D.
hasServiceTo
Indicates that one entity provides, offers, or operates a service for or directed toward another entity.
-
E.
hasSupported
Indicates that one entity has provided assistance, endorsement, or backing to another entity, either materially, emotionally, or through advocacy.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.