Triple
T29076217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camp 3 |
E735946
|
entity |
| Predicate | hasTypeOfConditions |
P194004
|
FINISHED |
| Object | malnutrition |
—
|
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: malnutrition | Statement: [Camp 3, hasTypeOfConditions, malnutrition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfConditions Context triple: [Camp 3, hasTypeOfConditions, malnutrition]
-
A.
hasNumberOfConditions
Indicates that an entity is associated with a specific count of conditions it has or is subject to.
-
B.
typeOfCondition
Indicates that one condition is a specific kind, category, or subtype of another condition.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasTypeOfCase
Indicates that an entity is associated with or classified under a particular type or category of case.
-
E.
treatsConditionType
Indicates that one entity (typically a treatment, procedure, or intervention) is used to address, manage, or cure a particular type or category of medical condition.
- F. None of above. chosen
Provenance (4 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_69f077e9b0a48190bb79548279cb7f64 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
| PDg | Predicate description generation | batch_69fd5d47da488190a4f2dbd44a0a83b2 |
completed | May 8, 2026, 3:49 a.m. |
Created at: April 28, 2026, 10:23 a.m.