Triple
T10368014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luke (Love Actually) |
E244305
|
entity |
| Predicate | weddingSceneAppearsIn |
P7326
|
FINISHED |
| Object | opening part of Love Actually |
—
|
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: opening part of Love Actually | Statement: [Luke (Love Actually), weddingSceneAppearsIn, opening part of Love Actually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weddingSceneAppearsIn Context triple: [Luke (Love Actually), weddingSceneAppearsIn, opening part of Love Actually]
-
A.
notableScene
chosen
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
B.
weddingCity
Indicates the city where a wedding takes place or is held.
-
C.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
D.
firstAppearanceScene
Indicates the scene in which an entity is shown or referenced for the first time within a work or sequence.
-
E.
performedInSceneType
Indicates that an action or event was carried out within a scene of a specified type or category.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97106448190a075948e63184f47 |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dface5508190a7b42f01ad0a19a2 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 12:01 p.m.