Triple
T1035459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Parris |
E22350
|
entity |
| Predicate | medicalDescription |
P11875
|
FINISHED |
| Object | experienced convulsions, screaming, and trance-like states |
—
|
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: experienced convulsions, screaming, and trance-like states | Statement: [Elizabeth Parris, medicalDescription, experienced convulsions, screaming, and trance-like states]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalDescription Context triple: [Elizabeth Parris, medicalDescription, experienced convulsions, screaming, and trance-like states]
-
A.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
B.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
healthcareType
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
D.
hasDescription
chosen
Indicates that an entity is associated with a textual description that explains or characterizes it.
-
E.
describedIn
Indicates that information about an entity is contained or documented within a specified source, such as a text, document, or media.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8d669448190955507e2e4975b9f |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b728ad3481909cf1430349cb9bba |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.