Triple
T7872132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Children's Health Defense |
E182761
|
entity |
| Predicate | positionOnVaccines |
P16734
|
FINISHED |
| Object | opposes routine childhood vaccination policies |
—
|
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: opposes routine childhood vaccination policies | Statement: [Children's Health Defense, positionOnVaccines, opposes routine childhood vaccination policies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: positionOnVaccines Context triple: [Children's Health Defense, positionOnVaccines, opposes routine childhood vaccination policies]
-
A.
positionOnHealthCare
Indicates a person or entity’s stance, opinion, or policy preference regarding health care systems, services, or reforms.
-
B.
positionOn
Indicates that one entity is located on top of or at a specific place along the surface or extent of another entity.
-
C.
positionOnGood
chosen
Indicates the stance or viewpoint an entity holds regarding a particular good, such as support, opposition, or neutrality.
-
D.
positionOnVitaminC
Indicates the stance, opinion, or policy that one entity holds regarding vitamin C.
-
E.
positionInCase
Indicates the specific role, status, or placement that an entity holds within a particular case or legal proceeding.
- 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_69ca82894d9081908a832bfce71a4714 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39a6d93881908d68386e49bea1e3 |
completed | March 31, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69cae928e1b88190b0620f4c4f03bc7d |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:56 p.m.