Triple
T32672308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kosaki Onodera |
E835329
|
entity |
| Predicate | hasChildhoodCrushOn |
P192977
|
FINISHED |
| Object | Raku Ichijou |
—
|
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: Raku Ichijou | Statement: [Kosaki Onodera, hasChildhoodCrushOn, Raku Ichijou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChildhoodCrushOn Context triple: [Kosaki Onodera, hasChildhoodCrushOn, Raku Ichijou]
-
A.
isChildhoodSweetheartOf
Indicates that two people were romantically involved with each other during their childhood or adolescence, typically as first or early sweethearts.
-
B.
hasFictionalRomanticInterest
chosen
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
C.
formerRomanticInterest
Indicates that one entity previously had a romantic relationship or attraction toward another entity, but that romantic connection has since ended.
-
D.
loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
-
E.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
- 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_69f3493134b48190aa3c8cb523bd3800 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fde49a084081909d99b1e0258169d5 |
completed | May 8, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69fde1d04bd881909a46ecbbf18dfe59 |
completed | May 8, 2026, 1:14 p.m. |
Created at: May 1, 2026, 1:09 a.m.