Triple
T30933260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Anderson |
E788050
|
entity |
| Predicate | causeOfBreakup |
P107317
|
FINISHED |
| Object | Pam Beesly’s growing relationship with Jim Halpert |
—
|
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: Pam Beesly’s growing relationship with Jim Halpert | Statement: [Roy Anderson, causeOfBreakup, Pam Beesly’s growing relationship with Jim Halpert]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfBreakup Context triple: [Roy Anderson, causeOfBreakup, Pam Beesly’s growing relationship with Jim Halpert]
-
A.
causeOfEndOfRelationship
Indicates that one entity is the reason or factor that led to the termination of a relationship between parties.
-
B.
breaksUpWith
Indicates that one entity ends a romantic or intimate relationship with another entity.
-
C.
breakup
Indicates the ending or dissolution of a romantic or close personal relationship between two entities.
-
D.
fictionalBreakupCause
chosen
Indicates the reason or circumstance within a fictional narrative that leads to a breakup between characters.
-
E.
breakupLinkedTo
Indicates a causal or associative relationship where a breakup is connected to, influenced by, or results from another event, factor, or entity.
- 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_69f224c0b7fc819090cb89df60d23653 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: April 29, 2026, 8:52 p.m.