Triple
T24429600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amanda Beckett |
E615955
|
entity |
| Predicate | hasExBoyfriend |
P90273
|
FINISHED |
| Object | Mike Dexter |
—
|
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: Mike Dexter | Statement: [Amanda Beckett, hasExBoyfriend, Mike Dexter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExBoyfriend Context triple: [Amanda Beckett, hasExBoyfriend, Mike Dexter]
-
A.
hasBoyfriend
Indicates that a person is in a romantic relationship where the other party is their boyfriend.
-
B.
exBoyfriendOf
chosen
Indicates that one person was formerly the romantic boyfriend of another person.
-
C.
hadPartner
Indicates that an entity was in a romantic or life-partner relationship with another entity at some point in time.
-
D.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
E.
hadPartnerType
Indicates that an entity was associated with another entity in a specific type or category of partnership.
- 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_69e2d7eadb248190a867130fe45f0388 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f296aab8948190b9cb869bab71fb4c |
completed | April 29, 2026, 11:39 p.m. |
| PD | Predicate disambiguation | batch_69f287cc4fd4819081e93cc638d9512d |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:15 a.m.