Triple
T2904850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arclight Cinemas |
E62738
|
entity |
| Predicate | brandCharacter |
P42647
|
FINISHED |
| Object | premium theater experience |
—
|
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: premium theater experience | Statement: [Arclight Cinemas, brandCharacter, premium theater experience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brandCharacter Context triple: [Arclight Cinemas, brandCharacter, premium theater experience]
-
A.
characterTheme
Indicates that a particular theme, motif, or conceptual focus is associated with a given character.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
characterAlias
Indicates that one character is known or referred to by an alternative name or alias.
-
D.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
E.
characterBasedOn
Indicates that one character is modeled, inspired, or derived from another real or fictional entity.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0cd68d48190aea4afbdaed2d4bc |
completed | March 7, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69abdd19bac881908f047d616aca8438 |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdd96670c8190b727f9ac27dadf67 |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 10:11 p.m.