Triple
T17693189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Kennedy |
E441086
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kennedy |
—
|
NE NERFINISHED |
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: Kennedy | Statement: [Susan Kennedy, familyName, Kennedy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kennedy Context triple: [Susan Kennedy, familyName, Kennedy]
-
A.
Kennedy
chosen
Kennedy is a prominent Irish-American political and public-service family best known for producing U.S. President John F. Kennedy and numerous other influential politicians.
-
B.
Kennedy
Kennedy is a given name shared by various notable individuals across fields such as politics, music, and entertainment.
-
C.
Kennedy
Kennedy is a vast rural federal electoral division in Queensland, Australia, known for encompassing remote outback communities and mining towns.
-
D.
JFK
JFK is John F. Kennedy International Airport, a major international air travel hub serving New York City.
-
E.
Presidente Kennedy
Presidente Kennedy is a small coastal municipality in the Brazilian state of Espírito Santo, known for its beaches and growing oil and gas-related economic activities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47153a5c8819095c36fd414167fb1 |
completed | April 19, 2026, 6:08 a.m. |
Created at: April 10, 2026, 10:03 a.m.