Triple
T1078417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juan Fernández firecrown |
E23890
|
entity |
| Predicate | sexualDimorphismType |
P1333
|
FINISHED |
| Object | strong plumage dimorphism |
—
|
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: strong plumage dimorphism | Statement: [Juan Fernández firecrown, sexualDimorphismType, strong plumage dimorphism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sexualDimorphismType Context triple: [Juan Fernández firecrown, sexualDimorphismType, strong plumage dimorphism]
-
A.
sexualDimorphism
chosen
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
B.
reproductiveType
Indicates the mode or strategy by which an organism reproduces or generates offspring.
-
C.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
D.
sexes
Indicates that one entity engages in sexual activity with another entity.
-
E.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b943b41481909b24050ca7e78971 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73ba8208190be7f3cef8c18689b |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.