Triple
T2366414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBE |
E47389
|
entity |
| Predicate | hasGenderNeutrality |
P37803
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [MBE, hasGenderNeutrality, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderNeutrality Context triple: [MBE, hasGenderNeutrality, yes]
-
A.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
B.
hasGenderDistinction
Indicates that a relationship, classification, or linguistic form differentiates entities based on gender categories.
-
C.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
D.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
E.
hasGenderInSomeTraditions
Indicates that, in at least some cultural, religious, or historical traditions, the subject is regarded as having a specific gender.
- 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_69a88a1a4a6081908645b0f2914521ab |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc749f8e0819094144b9dd9db8790 |
completed | March 7, 2026, 6:35 a.m. |
| PD | Predicate disambiguation | batch_69abc59b88348190a2d6c08f69974117 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc6443c6c8190b932de2abd8eb28f |
completed | March 7, 2026, 6:31 a.m. |
Created at: March 4, 2026, 7:55 p.m.