Triple
T10632678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raymond Chambers |
E250497
|
entity |
| Predicate | socialImpactArea |
P13201
|
FINISHED |
| Object | disease prevention |
—
|
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: disease prevention | Statement: [Raymond Chambers, socialImpactArea, disease prevention]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialImpactArea Context triple: [Raymond Chambers, socialImpactArea, disease prevention]
-
A.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
B.
socialConcern
Indicates a relationship where an entity is concerned about, attentive to, or actively engaged with social issues, problems, or well-being.
-
C.
socialIssue
chosen
Indicates a relationship where something is recognized or treated as a problem or concern affecting society or a community at large.
-
D.
society
Indicates a relationship in which individuals or groups are organized into a structured community bound by shared institutions, norms, or social systems.
-
E.
influencesCommunity
Indicates that one entity has an effect on the behavior, attitudes, or conditions of a community.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df95f5e88190b34ce3ec972759ef |
completed | April 8, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69d6dd83b114819098e84dc658e82d7e |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:02 p.m.