Triple
T32814544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Russell Girl |
E839247
|
entity |
| Predicate | mainCharacterMedicalCondition |
P93885
|
FINISHED |
| Object | leukemia |
—
|
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: leukemia | Statement: [The Russell Girl, mainCharacterMedicalCondition, leukemia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainCharacterMedicalCondition Context triple: [The Russell Girl, mainCharacterMedicalCondition, leukemia]
-
A.
clinicalCondition
Indicates that one entity has, exhibits, or is associated with a particular medical or health-related condition described by the other entity.
-
B.
hasHealthConcern
Indicates that an entity has a specific health-related issue, condition, or concern associated with it.
-
C.
settingOfIllness
Indicates the context, environment, or circumstances in which an illness occurs or manifests.
-
D.
isMedicallyRelevant
Indicates that something has significance, impact, or applicability within a medical or clinical context.
-
E.
hasProtagonistCondition
chosen
Indicates that the main character in a narrative has a particular condition, state, or affliction.
- 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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fcab6e888881908ca9e18660928a40 |
completed | May 7, 2026, 3:10 p.m. |
| PD | Predicate disambiguation | batch_69fc4562a5b88190bad48f083a6dcdfa |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 1, 2026, 1:15 a.m.