Triple
T34186144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenelle Evans |
E876964
|
entity |
| Predicate | hasNotableHealthIssue |
P4720
|
FINISHED |
| Object | publicly discussed mental health struggles |
—
|
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: publicly discussed mental health struggles | Statement: [Jenelle Evans, hasNotableHealthIssue, publicly discussed mental health struggles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableHealthIssue Context triple: [Jenelle Evans, hasNotableHealthIssue, publicly discussed mental health struggles]
-
A.
hasHealthConcern
chosen
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
B.
hasHealthCode
Indicates that an entity is associated with a specific health-related classification or status code.
-
C.
hasNotableIssue
Indicates that an entity is associated with a significant problem, concern, or defect that is noteworthy or exceptional compared to typical cases.
-
D.
hasHealthCareCharacteristic
Indicates that an entity possesses a specific healthcare-related attribute, quality, or feature.
-
E.
hasNoIssue
Indicates that there are no problems, defects, or conflicts associated with the referenced entity or situation.
- 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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0303622ce08190b359c9d247268729 |
completed | May 12, 2026, 10:39 a.m. |
| PD | Predicate disambiguation | batch_6a03030c02fc81908e546dba291b1240 |
completed | May 12, 2026, 10:38 a.m. |
Created at: May 1, 2026, 1:55 a.m.