Triple
T34949238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Hanks as adult Josh Baskin |
E1007941
|
entity |
| Predicate | playsSongInFamousScene |
P42255
|
FINISHED |
| Object | Heart and Soul |
—
|
NE NERFINISHED |
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: Heart and Soul | Statement: [Tom Hanks as adult Josh Baskin, playsSongInFamousScene, Heart and Soul]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playsSongInFamousScene Context triple: [Tom Hanks as adult Josh Baskin, playsSongInFamousScene, Heart and Soul]
-
A.
songFeaturedInFilm
chosen
Indicates that a particular song is included or used within a specific film.
-
B.
appearsInSongBy
Indicates that an entity is mentioned, referenced, or featured within a song created or performed by a specified artist or musical act.
-
C.
hasFictionalSong
Indicates that one entity includes, features, or is associated with a song that is fictional or exists only within a narrative context.
-
D.
sangSoundtrackFor
Indicates that one entity performed or recorded the soundtrack music for a work associated with another entity.
-
E.
parallelSongInFilm
Indicates that a song is used in a film in a way that parallels or mirrors another narrative, emotional, or visual element within that film.
- 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_69f76dc5d4308190b77553ee07b1ede6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782ccb1ec8190a15e00c9e678e5da |
completed | May 3, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f781020cc4819088c40cb8589504e4 |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.