Triple
T35636459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rey Mysterio vs. Dominik Mysterio |
E1029732
|
entity |
| Predicate | involvesRealLifeRelationship |
P196375
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Rey Mysterio vs. Dominik Mysterio, involvesRealLifeRelationship, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesRealLifeRelationship Context triple: [Rey Mysterio vs. Dominik Mysterio, involvesRealLifeRelationship, true]
-
A.
starsRealLifeRelationship
Indicates that one entity is romantically involved with another entity in real life, outside of any fictional or on-screen roles.
-
B.
realLifeRelationshipWithAngelinaJolie
Indicates that there exists a real-life personal relationship or connection between the subject and Angelina Jolie.
-
C.
followedRealLifeFamily
Indicates that one person chose to follow a family member from their real life on a platform or service.
-
D.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
E.
sexualRelationshipTo
Indicates that one entity has engaged in a sexual relationship or sexual activity with another entity.
- F. None of above. chosen
Provenance (4 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_69f76e087bdc8190a4794bf9c0bd7634 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe2f078c24819082ba396b56f02808 |
completed | May 8, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69fe228fe1988190baf3bb34897f3dbe |
completed | May 8, 2026, 5:51 p.m. |
| PDg | Predicate description generation | batch_69fe2f066ac48190882c327b7e69e53f |
completed | May 8, 2026, 6:44 p.m. |
Created at: May 3, 2026, 4:05 p.m.