Triple
T34999772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Deane |
E1009638
|
entity |
| Predicate | isAuntWithin |
P182196
|
FINISHED |
| Object | extended Tulliver-Dodson family |
—
|
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: extended Tulliver-Dodson family | Statement: [Mrs. Deane, isAuntWithin, extended Tulliver-Dodson family]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAuntWithin Context triple: [Mrs. Deane, isAuntWithin, extended Tulliver-Dodson family]
-
A.
hasAunt
Indicates that one entity is the aunt of another, typically meaning a sister (or sister-in-law) of a parent of that entity.
-
B.
auntOf
Indicates that one person is the aunt of another, typically as the sibling (or sibling-in-law) of a parent or sometimes an older female relative in an extended family role.
-
C.
uncleOrAuntOf
Indicates that one person is the uncle or aunt of another person, typically as the sibling (or sibling-in-law) of the other person’s parent.
-
D.
maternalAuntOrUncle
Indicates that one person is the sibling of another person's mother, regardless of the sibling's gender.
-
E.
relationshipToAuntEller
Indicates the specific familial relationship that an entity has to Aunt Eller (e.g., whether and how they are related to her).
- F. None of above. chosen
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_69f76dcb716881909f75e4fd60ab2284 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7870dfe108190996c0c68630edc7f |
completed | May 3, 2026, 5:34 p.m. |
Created at: May 3, 2026, 4:01 p.m.