Triple
T30083889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Job's family |
E764547
|
entity |
| Predicate | hasNumberOfChildrenBeforeCalamity |
P193662
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Job's family, hasNumberOfChildrenBeforeCalamity, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfChildrenBeforeCalamity Context triple: [Job's family, hasNumberOfChildrenBeforeCalamity, 10]
-
A.
hasChildrenBeforeImmortality
Indicates that an entity had one or more children prior to becoming immortal.
-
B.
numberOfChildrenSurvivors
Indicates the count of children who survived a particular event, condition, or situation.
-
C.
hasChildCasualties
Indicates that an event, incident, or situation resulted in casualties specifically involving children.
-
D.
hasChildWhoBecame
Indicates that an entity has a child who later attained or transitioned into a specified role, status, or condition.
-
E.
numberOfChildVictims
Indicates the count of individuals who are victims and are classified as children in the context of the described event or situation.
- 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69fd4f38728c8190b3271abc80882cfb |
completed | May 8, 2026, 2:49 a.m. |
Created at: April 29, 2026, 7:03 p.m.