Triple
T26295364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vanessa Carlysle |
E661399
|
entity |
| Predicate | primaryLoveInterestOf |
P7325
|
FINISHED |
| Object | Deadpool |
—
|
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: Deadpool | Statement: [Vanessa Carlysle, primaryLoveInterestOf, Deadpool]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryLoveInterestOf Context triple: [Vanessa Carlysle, primaryLoveInterestOf, Deadpool]
-
A.
loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
-
B.
formerRomanticInterest
Indicates that one entity previously had a romantic relationship or attraction toward another entity, but that romantic connection has since ended.
-
C.
loveInterestType
Indicates the specific kind or category of romantic or affectionate relationship that exists between the related entities.
-
D.
loveInterest
chosen
Indicates that one entity is the romantic object of affection or attraction for another entity.
-
E.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
- 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_69ee812cd48c81908054068f545f0526 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69fd9ff026a48190bfec33deeb3b2c43 |
completed | May 8, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69fd97d805bc8190ba12f429d3ad04c7 |
completed | May 8, 2026, 7:59 a.m. |
Created at: April 26, 2026, 10:11 p.m.