Triple
T11390927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMC Classic |
E269832
|
entity |
| Predicate | parentOrganization |
P254
|
FINISHED |
| Object | AMC Theatres |
E55106
|
NE 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: AMC Theatres | Statement: [AMC Classic, parentOrganization, AMC Theatres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AMC Theatres Context triple: [AMC Classic, parentOrganization, AMC Theatres]
-
A.
AMC Theatres
chosen
AMC Theatres is one of the largest movie theater chains in the world, operating multiplex cinemas across the United States and internationally.
-
B.
Cinemark Theatres
Cinemark Theatres is a major American movie theater chain operating multiplex cinemas across the United States and in several Latin American countries.
-
C.
United Cinemas
United Cinemas is a Japanese movie theater chain operating multiplex cinemas in various locations, including major shopping and entertainment complexes.
-
D.
Marcus Theatres
Marcus Theatres is a major American movie theater chain known for operating multiplex cinemas across the Midwest and other regions of the United States.
-
E.
Regal Cinemas
Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b813cfc48190a10d9b78953112ae |
completed | April 20, 2026, 5:22 a.m. |
Created at: April 8, 2026, 9:34 p.m.