Triple
T33670297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cole Trickle |
E862598
|
entity |
| Predicate | hasLoveInterestInFilm |
P177260
|
FINISHED |
| Object | Dr. Claire Lewicki |
—
|
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: Dr. Claire Lewicki | Statement: [Cole Trickle, hasLoveInterestInFilm, Dr. Claire Lewicki]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoveInterestInFilm Context triple: [Cole Trickle, hasLoveInterestInFilm, Dr. Claire Lewicki]
-
A.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
B.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
C.
isRomanticLeadOf
chosen
Indicates that one entity serves as the primary romantic partner or love-interest counterpart to another entity within a narrative or story.
-
D.
loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
-
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_69f34984c4008190bb82f33a7819da64 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: May 1, 2026, 1:42 a.m.