Triple
T8357262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guillain–Barré syndrome |
E196711
|
entity |
| Predicate | hasSexDistribution |
P63667
|
FINISHED |
| Object | slightly more common in males |
—
|
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: slightly more common in males | Statement: [Guillain–Barré syndrome, hasSexDistribution, slightly more common in males]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSexDistribution Context triple: [Guillain–Barré syndrome, hasSexDistribution, slightly more common in males]
-
A.
hasGenderDistributionIssues
Indicates that the entity exhibits problems, imbalances, or inequities related to the distribution or representation of different genders.
-
B.
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.
-
C.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
D.
hasGenderDivisions
Indicates that something is organized, classified, or separated into groups based on gender.
-
E.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
- 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_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb804b57f88190907a4e4e389caf5f |
completed | March 31, 2026, 8:05 a.m. |
| PD | Predicate disambiguation | batch_69cb70ca25548190b0f90c5384e3fb3c |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:59 p.m.