Triple
T14144609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Story of the Red Cross |
E350511
|
entity |
| Predicate | workInfluence |
P1994
|
FINISHED |
| Object | public understanding of the American Red Cross |
—
|
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: public understanding of the American Red Cross | Statement: [A Story of the Red Cross, workInfluence, public understanding of the American Red Cross]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workInfluence Context triple: [A Story of the Red Cross, workInfluence, public understanding of the American Red Cross]
-
A.
workImpact
Indicates that one entity’s work has an effect or influence on another entity, situation, or outcome.
-
B.
influencedWork
chosen
Indicates that one work has had a significant impact on the creation, style, content, or development of another work.
-
C.
placeOfInfluence
Indicates the location or area where an entity exerts significant impact, authority, or cultural, social, or intellectual influence.
-
D.
typeOfInfluence
Indicates the specific nature or category of influence that one entity exerts on another.
-
E.
exploresInfluence
Indicates that one entity investigates, examines, or analyzes the impact or effect that another 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61214de081909a5186ff11336f97 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:53 a.m.