Triple
T34368895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Lindsay Hiddingh |
E882097
|
entity |
| Predicate | numberOfChildrenWhoDiedWithHer |
P205411
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Mary Lindsay Hiddingh, numberOfChildrenWhoDiedWithHer, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildrenWhoDiedWithHer Context triple: [Mary Lindsay Hiddingh, numberOfChildrenWhoDiedWithHer, 2]
-
A.
numberOfChildrenAtDeath
Indicates the total count of a person's children at the time of their death.
-
B.
hasUnbornChildAtDeath
Indicates that, at the time of an individual’s death, they had at least one child who had been conceived but was not yet born.
-
C.
hadNoSurvivingChildren
Indicates that the person did not have any children who were alive at the relevant point in time.
-
D.
numberOfChildrenSurvivors
Indicates the count of children who survived a particular event, condition, or situation.
-
E.
numberOfChildrenBorn
Indicates the total count of children that have been born to a given parent or entity.
- 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_69f349be5c9c81908dc726ae1f4c68f2 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c80ba448190853011097a151b7e |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:58 a.m.