Triple
T32074572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kon Ichikawa |
E819106
|
entity |
| Predicate | hasSpouseCollaboration |
P70306
|
FINISHED |
| Object | screenplays written by Natto Wada |
—
|
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: screenplays written by Natto Wada | Statement: [Kon Ichikawa, hasSpouseCollaboration, screenplays written by Natto Wada]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpouseCollaboration Context triple: [Kon Ichikawa, hasSpouseCollaboration, screenplays written by Natto Wada]
-
A.
hasCollaborativeRoleWithSpouse
Indicates that an individual shares a joint, cooperative role or responsibility together with their spouse.
-
B.
spouseWorkWith
Indicates that a person’s spouse works together with a specified person, typically as colleagues in the same workplace or professional context.
-
C.
hasAuthorSpouse
chosen
Indicates that the spouse of the subject entity is the author of the related work or entity.
-
D.
hasSpouseActorsInLeads
Indicates that the primary leading roles in a work are performed by actors who are spouses of each other.
-
E.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
- 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_69f348fecc088190af1470afe5a969f0 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
Created at: May 1, 2026, 12:23 a.m.