Triple
T26899358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sex, God & Marriage |
E677983
|
entity |
| Predicate | seesMarriageAs |
P161039
|
FINISHED |
| Object | lifelong union of husband and wife |
—
|
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: lifelong union of husband and wife | Statement: [Sex, God & Marriage, seesMarriageAs, lifelong union of husband and wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seesMarriageAs Context triple: [Sex, God & Marriage, seesMarriageAs, lifelong union of husband and wife]
-
A.
regardsMarriageAs
chosen
Indicates that one entity holds a particular view, attitude, or evaluative stance toward the institution or concept of marriage.
-
B.
hasAttitudeTowardMarriage
Indicates that an entity holds a particular opinion, feeling, or stance regarding the institution or concept of marriage.
-
C.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
D.
acceptsMarriageTo
Indicates that one entity formally agrees to enter into a marriage with another entity.
-
E.
marriageSignificance
Indicates the importance, impact, or meaningfulness that a marriage holds within a given context or for the entities involved.
- 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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61fd623bc819091df736cf3419b99 |
completed | May 2, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69f61b3d23f481908dfec27adace900a |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 5:49 a.m.