Triple
T5043658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Asphalt Jungle (role as Dix Handley) |
E113606
|
entity |
| Predicate | filmBlackAndWhite |
P3490
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [The Asphalt Jungle (role as Dix Handley), filmBlackAndWhite, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmBlackAndWhite Context triple: [The Asphalt Jungle (role as Dix Handley), filmBlackAndWhite, true]
-
A.
blackAndWhite
chosen
Indicates that something is presented or exists in only black and white, without any other colors.
-
B.
filmMedium
Indicates the physical or technical format (such as film stock, digital, or video) in which a film is recorded or presented.
-
C.
film
Indicates that an entity is a movie or cinematic work, or that a relationship involves such a movie.
-
D.
filmType
Indicates the specific category or genre that a film belongs to.
-
E.
filmBase
Indicates the primary location or headquarters from which a film-related entity (such as a production, company, or operation) is based or operates.
- 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fc04f08190aba851fa0192d0fb |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:37 p.m.