Triple
T28487793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max and Paddy's Road to Nowhere |
E720878
|
entity |
| Predicate | featuresProfessionOfMainCharacters |
P21567
|
FINISHED |
| Object | bouncers |
—
|
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: bouncers | Statement: [Max and Paddy's Road to Nowhere, featuresProfessionOfMainCharacters, bouncers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresProfessionOfMainCharacters Context triple: [Max and Paddy's Road to Nowhere, featuresProfessionOfMainCharacters, bouncers]
-
A.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
B.
featuresCharacterRole
Indicates that a work includes a character appearing in a specific narrative or functional role.
-
C.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
D.
producerCharacter
Indicates that a producer is responsible for or associated with a particular character in a work.
-
E.
filmCharacterDescribedAs
Indicates that a film character is described or characterized using a particular attribute, phrase, or depiction.
- 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_69f01a5a47148190b0a7e111bc432e0a |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
Created at: April 28, 2026, 2:59 a.m.