Triple
T31535315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | multiple system atrophy |
E804586
|
entity |
| Predicate | hasSexRatio |
P63667
|
FINISHED |
| Object | slight male predominance |
—
|
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: slight male predominance | Statement: [multiple system atrophy, hasSexRatio, slight male predominance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSexRatio Context triple: [multiple system atrophy, hasSexRatio, slight male predominance]
-
A.
hasGenderRatioMale
Indicates the proportion or percentage of male individuals within a given population or group.
-
B.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
C.
hasGenderRatioFemale
Indicates the proportion or percentage of females relative to the total population in the described group or context.
-
D.
hasSexPredominance
chosen
Indicates that one sex (male or female) is more commonly or predominantly associated with the given condition, trait, or occurrence than the other.
-
E.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
- 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69ff1a972bf08190860696ffcd887c0f |
completed | May 9, 2026, 11:29 a.m. |
| PD | Predicate disambiguation | batch_69ff184005d88190bf38283ebc499b28 |
completed | May 9, 2026, 11:19 a.m. |
Created at: April 30, 2026, 10:03 p.m.