Triple
T35481841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Sister |
E1025489
|
entity |
| Predicate | genderAssociationInText |
P183787
|
FINISHED |
| Object | more slender and lighter than Blackfyre |
—
|
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: more slender and lighter than Blackfyre | Statement: [Dark Sister, genderAssociationInText, more slender and lighter than Blackfyre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderAssociationInText Context triple: [Dark Sister, genderAssociationInText, more slender and lighter than Blackfyre]
-
A.
genderImplication
Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
-
B.
genderDivision
Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on gender.
-
C.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
-
D.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
E.
hasGenderInText
Indicates that a specified gender is explicitly mentioned or assigned to an entity within a given text.
- F. None of above. chosen
Provenance (4 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_69f76dfadba0819083456aadcd6864ea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a283388c81908e4a9ee3369e8d6f |
completed | May 3, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a224365081908ff6958e3b30bd05 |
completed | May 3, 2026, 7:29 p.m. |
Created at: May 3, 2026, 4:04 p.m.