Triple
T16779822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosalind Connage |
E407827
|
entity |
| Predicate | relationshipOutcomeWithAmoryBlaine |
P124605
|
FINISHED |
| Object | breaks off 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: breaks off relationship | Statement: [Rosalind Connage, relationshipOutcomeWithAmoryBlaine, breaks off relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipOutcomeWithAmoryBlaine Context triple: [Rosalind Connage, relationshipOutcomeWithAmoryBlaine, breaks off relationship]
-
A.
relationshipTypeWithJakeBarnes
Indicates the specific nature or category of relationship that an entity has with Jake Barnes.
-
B.
relationshipToSamanthaGrimm
Indicates the specific type of relationship or connection an entity has to Samantha Grimm.
-
C.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
D.
relationshipStatusWithMichael
Indicates the type or state of the relationship that an entity currently has with Michael.
-
E.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b214cebc81909de80e74b4bac5f8 |
completed | April 18, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69e319cf691c819083e39225f5777ef0 |
completed | April 18, 2026, 5:42 a.m. |
| PDg | Predicate description generation | batch_69e326bac94481908c082117553320f8 |
completed | April 18, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:22 a.m.