Triple
T30932933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jan Levinson |
E788043
|
entity |
| Predicate | relationshipStatusWithMichaelScott |
P93770
|
FINISHED |
| Object | on-and-off romantic relationship |
—
|
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: on-and-off romantic relationship | Statement: [Jan Levinson, relationshipStatusWithMichaelScott, on-and-off romantic relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWithMichaelScott Context triple: [Jan Levinson, relationshipStatusWithMichaelScott, on-and-off romantic relationship]
-
A.
relationshipStatusWithMichael
chosen
Indicates the type or state of the relationship that an entity currently has with Michael.
-
B.
relationshipToPamBeesly
Indicates the specific type of personal or social relationship an entity has with Pam Beesly.
-
C.
relationshipToMike
Indicates the specific type of personal, social, or familial relationship that an entity has with Mike.
-
D.
relationshipToMichelle
Indicates the specific type of relationship or connection that an entity has to Michelle.
-
E.
relationshipStatusWithSam
Indicates the type or state of the relationship that an entity currently has with Sam.
- 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_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e2fac0819089b522db3260028c |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 29, 2026, 8:52 p.m.