Triple
T4501869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peggy Fleming |
E101238
|
entity |
| Predicate | healthEvent |
P4720
|
FINISHED |
| Object | breast cancer diagnosis in 1998 |
—
|
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: breast cancer diagnosis in 1998 | Statement: [Peggy Fleming, healthEvent, breast cancer diagnosis in 1998]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthEvent Context triple: [Peggy Fleming, healthEvent, breast cancer diagnosis in 1998]
-
A.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
B.
healthIndicator
Indicates a measure or signal that reflects the health status or condition of an entity.
-
C.
emergencyEvent
Indicates that an urgent, unexpected, and potentially harmful situation or incident has occurred requiring immediate attention or response.
-
D.
hasHealthConcern
chosen
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
E.
publicHealthResponse
Indicates actions and measures taken by authorities or organizations to prevent, control, or mitigate health threats within a population.
- 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_69bd43d175248190894dc58b5b395c26 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56f9dca08190b926f40e201a3e97 |
completed | March 20, 2026, 2:17 p.m. |
| PD | Predicate disambiguation | batch_69bd521671688190bc655d25fa77eba2 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1 p.m.