Triple
T17251682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thals |
E418767
|
entity |
| Predicate | genderDimorphism |
P2577
|
FINISHED |
| Object | male and female |
—
|
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: male and female | Statement: [Thals, genderDimorphism, male and female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderDimorphism Context triple: [Thals, genderDimorphism, male and female]
-
A.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
B.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
C.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
D.
genderCategories
chosen
Indicates the classification of an entity into one or more gender-related categories or identities.
-
E.
genderDivision
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e694f788190a1c86e95264ed2fe |
completed | April 19, 2026, 1:22 a.m. |
| PD | Predicate disambiguation | batch_69e3832a284481908a8a3da7ac91de5a |
completed | April 18, 2026, 1:12 p.m. |
Created at: April 10, 2026, 5:39 a.m.