Triple
T32888324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panini |
E841262
|
entity |
| Predicate | hasRomanticGoal |
P138576
|
FINISHED |
| Object | to marry Chowder (character) |
—
|
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: to marry Chowder (character) | Statement: [Panini, hasRomanticGoal, to marry Chowder (character)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticGoal Context triple: [Panini, hasRomanticGoal, to marry Chowder (character)]
-
A.
relationshipGoal
Indicates that one entity has a desired or intended relationship status or type involving another entity.
-
B.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
C.
romanticRelationshipStatus
Indicates the nature or state of a romantic relationship between entities, such as whether they are dating, committed, separated, or otherwise romantically involved.
-
D.
desiredMarriageWith
chosen
Indicates that one entity wishes to enter into a marital relationship with another entity.
-
E.
hasRomanticEntanglementInPlot
Indicates that a romantic relationship or involvement between characters is a significant element within the narrative plot.
- 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_69f349446e288190a70c05bcc4d81172 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
Created at: May 1, 2026, 1:18 a.m.