Triple
T5812900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alex Vause |
E128912
|
entity |
| Predicate | relationshipStatusWith Piper Chapman |
P61561
|
FINISHED |
| Object | engaged (later in series) |
—
|
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: engaged (later in series) | Statement: [Alex Vause, relationshipStatusWith Piper Chapman, engaged (later in series)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipStatusWith Piper Chapman Context triple: [Alex Vause, relationshipStatusWith Piper Chapman, engaged (later in series)]
-
A.
relationshipStatusDuringFilm
Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
-
B.
inRelationshipWith
chosen
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
-
C.
maritalRelations
Indicates a legally or socially recognized spousal relationship or marriage-based connection between two entities.
-
D.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
E.
relationshipToHannah
Indicates the specific type of relationship or connection that an entity has to Hannah.
- 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_69c0084788848190bcf71f6bc5d71597 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b56044c8190847478a342d441c2 |
completed | March 22, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c021d5ecd081908a62dd66e26f8598 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:52 p.m.