Triple
T34252921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grandma Moses |
E878796
|
entity |
| Predicate | childrenSurvivingToAdulthood |
P15463
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Grandma Moses, childrenSurvivingToAdulthood, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: childrenSurvivingToAdulthood Context triple: [Grandma Moses, childrenSurvivingToAdulthood, 5]
-
A.
childrenSurvivedToAdulthood
chosen
Indicates that the referenced children lived beyond childhood and reached adulthood.
-
B.
numberOfChildrenSurvivors
Indicates the count of children who survived a particular event, condition, or situation.
-
C.
survivingChildOfParents
Indicates that one entity is a child who remains alive after the death of the specified parent or parents.
-
D.
childrenFounded
Indicates that the subject’s children established, created, or founded the object entity (such as an organization, institution, or project).
-
E.
numberOfAdultVictims
Indicates the count of adult individuals who are victims in the described event or situation.
- 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_69f349b3618481909df955b063f305b2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71362f1448190985a80ce7af475cb |
completed | May 3, 2026, 9:20 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
Created at: May 1, 2026, 1:56 a.m.