Triple
T4954268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahlon |
E111242
|
entity |
| Predicate | marriageLeadsTo |
P23691
|
FINISHED |
| Object | Ruth’sIntegrationIntoIsrael |
—
|
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: Ruth’sIntegrationIntoIsrael | Statement: [Mahlon, marriageLeadsTo, Ruth’sIntegrationIntoIsrael]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageLeadsTo Context triple: [Mahlon, marriageLeadsTo, Ruth’sIntegrationIntoIsrael]
-
A.
marries
chosen
Indicates that one entity enters into a legally or socially recognized marital union with another entity.
-
B.
marriageContext
Indicates the situational or cultural circumstances under which a marriage occurs or exists, such as legal, social, or religious conditions surrounding the marital relationship.
-
C.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
D.
hasMarriage
Indicates a marital relationship exists between the two entities, specifying that they are or were legally married to each other.
-
E.
marriageCharacterization
Indicates how a marriage is described, evaluated, or characterized in terms of its qualities, dynamics, or nature.
- 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_69bd4418390c8190b7e9766a2512ce55 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd71b945c0819082723712905f8295 |
completed | March 20, 2026, 4:11 p.m. |
| PD | Predicate disambiguation | batch_69bd6c3beb008190852fd6150813b252 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:32 p.m.