Triple
T32089242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Séverine Serizy |
E819539
|
entity |
| Predicate | relationshipToHusband |
P196518
|
FINISHED |
| Object | emotionally distant |
—
|
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: emotionally distant | Statement: [Séverine Serizy, relationshipToHusband, emotionally distant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToHusband Context triple: [Séverine Serizy, relationshipToHusband, emotionally distant]
-
A.
relationshipToSpouse
Indicates the specific familial or social role one person holds in relation to their spouse (e.g., husband, wife, partner).
-
B.
spouseRelationshipType
chosen
Indicates the specific nature or category of a marital or spousal relationship between two individuals.
-
C.
spouseRelative
Indicates that one person is related to another through the marriage of at least one of them (e.g., in-laws or relatives by marriage).
-
D.
spouseRelationshipContext
Indicates a marital relationship context between two entities, specifying that they are spouses or partners in a recognized marriage-like union.
-
E.
maritalRelations
Indicates a legally or socially recognized spousal relationship or marriage-based connection between two entities.
- 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_69f349004b2481908ce2e50af0d579a8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:25 a.m.