Triple
T15593106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irene Heron |
E374795
|
entity |
| Predicate | spouseRelationshipCharacterization |
P21095
|
FINISHED |
| Object | unhappy marriage |
—
|
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: unhappy marriage | Statement: [Irene Heron, spouseRelationshipCharacterization, unhappy marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseRelationshipCharacterization Context triple: [Irene Heron, spouseRelationshipCharacterization, unhappy marriage]
-
A.
spouseCharacteristic
Indicates that a particular characteristic, trait, or attribute is associated with a person’s spouse within the relationship.
-
B.
marriageCharacterization
chosen
Indicates how a marriage is described, evaluated, or characterized in terms of its qualities, dynamics, or nature.
-
C.
spouseRelationshipContext
Indicates a marital relationship context between two entities, specifying that they are spouses or partners in a recognized marriage-like union.
-
D.
spouseType
Indicates the specific role or category of a person within a spousal relationship (e.g., husband, wife, partner).
-
E.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e5e43d48190a8fd367f13f1c7e1 |
completed | April 16, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69deda817e9881909b0c66fc9056f7d5 |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:12 a.m.