Triple
T2100860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flavius |
E37087
|
entity |
| Predicate | variantGender |
P12026
|
FINISHED |
| Object | Flavia is the feminine form |
—
|
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: Flavia is the feminine form | Statement: [Flavius, variantGender, Flavia is the feminine form]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: variantGender Context triple: [Flavius, variantGender, Flavia is the feminine form]
-
A.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
B.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
C.
genderUsage
Indicates how a particular gender is applied, referenced, or treated within a given context or system.
-
D.
hasGenderDistinction
chosen
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
E.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
- 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_69a8861828948190924aa30c08806b3a |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abbabb512c819087b1d5ec8ef11e63 |
completed | March 7, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69abb7b6274081909df36cd7a7c6a675 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.