Triple
T26552066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jane Bond |
E671702
|
entity |
| Predicate | hasFamilyConnectionByMarriageWith |
P167266
|
FINISHED |
| Object | Alice Clopton |
—
|
NE NERFINISHED |
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: Alice Clopton | Statement: [Jane Bond, hasFamilyConnectionByMarriageWith, Alice Clopton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyConnectionByMarriageWith Context triple: [Jane Bond, hasFamilyConnectionByMarriageWith, Alice Clopton]
-
A.
connectedThroughMarriageVia
Indicates that two entities are related to each other by a marital connection that is mediated through one or more intermediate spouses or in-laws, rather than by a direct marriage between them.
-
B.
hasMemberSpouseConnection
Indicates a relationship where one entity is a spouse of a member associated with the other entity.
-
C.
hasNephewByMarriage
Indicates that one person is the nephew of another person through marriage rather than by blood.
-
D.
maritalRelations
Indicates a legally or socially recognized spousal relationship or marriage-based connection between two entities.
-
E.
characterRelativeByMarriage
chosen
Indicates that one character is related to another through marriage rather than by blood.
- 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_69eeb32163f08190af5f81282738e27a |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b78f29481908cc8f390496dee97 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 27, 2026, 1:47 a.m.