Triple
T5080485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pikachu |
E114497
|
entity |
| Predicate | hasGenderDifference |
P12026
|
FINISHED |
| Object | female has heart-shaped tail end |
—
|
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: female has heart-shaped tail end | Statement: [Pikachu, hasGenderDifference, female has heart-shaped tail end]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderDifference Context triple: [Pikachu, hasGenderDifference, female has heart-shaped tail end]
-
A.
hasGenderDistinction
chosen
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
B.
hasGenderVariant
Indicates that one entity is a gender-specific form or variant of another entity.
-
C.
hasGenderSystem
Indicates that an entity employs or is characterized by a particular system for categorizing gender.
-
D.
hasGenderInSomeTraditions
Indicates that, in at least some cultural, religious, or historical traditions, the subject is regarded as having a specific gender.
-
E.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of 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_69bd443dbf908190a9401e9c2dc7bd7d |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74f86c988190aa026073ed435a45 |
completed | March 20, 2026, 4:25 p.m. |
| PD | Predicate disambiguation | batch_69bd7159adc881909effd4382c395c66 |
completed | March 20, 2026, 4:10 p.m. |
Created at: March 20, 2026, 1:39 p.m.