Triple
T10598039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catelynn Lowell |
E275667
|
entity |
| Predicate | hasMentalHealthFocus |
P4720
|
FINISHED |
| Object | depression |
—
|
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: depression | Statement: [Catelynn Lowell, hasMentalHealthFocus, depression]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMentalHealthFocus Context triple: [Catelynn Lowell, hasMentalHealthFocus, depression]
-
A.
focusesOnMedicalCare
Indicates that one entity directs attention, resources, or activity specifically toward providing or improving medical care for another entity.
-
B.
hasImpactFocus
Indicates that an entity is primarily concerned with or directed toward a particular type or area of impact.
-
C.
hasTherapeuticGoal
Indicates that an action, treatment, or intervention is undertaken with the intention of achieving a specific therapeutic or health-related outcome.
-
D.
hasCognitiveComponent
Indicates that the related entity or process involves or depends on mental activities such as thinking, reasoning, perception, or understanding.
-
E.
hasHealthConcern
chosen
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4992248190b640d743ccf02c82 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
Created at: April 8, 2026, 7:28 p.m.