Triple
T20079285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mother Angelica |
E499954
|
entity |
| Predicate | hadMajorStroke |
P104846
|
FINISHED |
| Object | 2001 |
—
|
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: 2001 | Statement: [Mother Angelica, hadMajorStroke, 2001]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadMajorStroke Context triple: [Mother Angelica, hadMajorStroke, 2001]
-
A.
stroke
Indicates that one entity moves its hand or an object gently or repeatedly over the surface of another entity.
-
B.
hadMajorFront
Indicates that an entity (such as a conflict or war) involved a significant primary front or theater of operations in a specified location or context.
-
C.
hasMajorMotorRace
Indicates that a location or entity regularly hosts or is the site of a significant, high-profile motor racing event.
-
D.
hasHistoryOf
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
-
E.
hadCondition
chosen
Indicates that an entity experienced or was diagnosed with a particular medical or health-related condition.
- 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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6643f93208190ae2a413f88ea9aed |
completed | April 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69e54cf369b88190931532420517dac7 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:40 p.m.