Triple
T3009637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scooby-Doo (2002 film) |
E81984
|
entity |
| Predicate | featuresMedium |
P11606
|
FINISHED |
| Object | computer-generated imagery |
—
|
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: computer-generated imagery | Statement: [Scooby-Doo (2002 film), featuresMedium, computer-generated imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresMedium Context triple: [Scooby-Doo (2002 film), featuresMedium, computer-generated imagery]
-
A.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
B.
featuresStar
Indicates that one entity prominently includes or showcases another entity as a main star or featured performer.
-
C.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
D.
featuresCross
Indicates that one feature or element intersects or passes across another in space or structure.
-
E.
secondaryMedium
chosen
Indicates that an entity is associated with an additional, non-primary medium or channel through which it is expressed, delivered, or communicated.
- 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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a4ccbf08190a7580c9e758804d0 |
completed | March 8, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3 p.m.