Triple
T22193327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Marie Hennen |
E548485
|
entity |
| Predicate | numberOfChildrenWithJohnBellHood |
P289
|
FINISHED |
| Object | large postwar 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: large postwar family | Statement: [Anna Marie Hennen, numberOfChildrenWithJohnBellHood, large postwar family]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildrenWithJohnBellHood Context triple: [Anna Marie Hennen, numberOfChildrenWithJohnBellHood, large postwar family]
-
A.
numberOfChildren
chosen
Indicates the total count of children that an entity has.
-
B.
numberOfChildrenSurvivors
Indicates the count of children who survived a particular event, condition, or situation.
-
C.
numberOfChildrenMurdered
Indicates the count of children who have been killed in an act of murder.
-
D.
numberOfChildVictims
Indicates the count of individuals who are victims and are classified as children in the context of the described event or situation.
-
E.
numberOfSons
Indicates the count of male offspring that an entity has.
- 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12ae552908190b38c3d765bcfb68a |
completed | April 28, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e71b48576c8190a8e93738fd9cfda5 |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 8:35 p.m.