Triple
T14567342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armageddon Time |
E341817
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object | RT Features |
E268527
|
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: RT Features | Statement: [Armageddon Time, productionCompany, RT Features]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RT Features Context triple: [Armageddon Time, productionCompany, RT Features]
-
A.
RT Features
chosen
RT Features is a Brazilian film production company known for backing acclaimed independent and auteur-driven films, including collaborations with prominent international directors.
-
B.
iFeatures
iFeatures is a UK-based low-budget film initiative and production scheme that supports emerging filmmakers in developing and producing feature films.
-
C.
FeatureSpec
FeatureSpec is a ScalaTest testing style that lets you write behavior-driven tests organized around high-level features and their scenarios.
-
D.
Foundation Features
Foundation Features is a film production company known for producing the biographical drama "Brain on Fire."
-
E.
RT
RT is the commonly used abbreviation for Rotten Tomatoes, a popular website that aggregates film and television reviews and ratings.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb38d89fc819086709fd3607b835f |
completed | April 14, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ac669cc819083e05620b1e8c370 |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:23 a.m.