Triple
T35962663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joanna Bennett |
E1040043
|
entity |
| Predicate | hasFamilyConnectionByMarriageTo |
P7844
|
FINISHED |
| Object | Susan Crow |
E291900
|
NE 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: Susan Crow | Statement: [Joanna Bennett, hasFamilyConnectionByMarriageTo, Susan Crow]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamilyConnectionByMarriageTo Context triple: [Joanna Bennett, hasFamilyConnectionByMarriageTo, Susan Crow]
-
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.
marriageLinkedBranch
Indicates a connection between two branches or lineages that is established or traced through a marriage relationship.
-
D.
hasFamilialTieTo
chosen
Indicates a relationship where two entities are connected by family bonds, such as by blood, marriage, or adoption.
-
E.
hasNephewByMarriage
Indicates that one person is the nephew of another person through marriage rather than by blood.
- F. None of above.
Provenance (4 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_69f76e26b21081909fd9ffb3aff6c77a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe5c1a502081909d4024e514309c8e |
completed | May 8, 2026, 9:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38b70935608190848aa90f1ed84f7b |
completed | June 22, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69fe5a9df21c819087153f5d0bcaa987 |
completed | May 8, 2026, 9:50 p.m. |
Created at: May 3, 2026, 4:07 p.m.