Triple
T15854057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crown Prince of Belgium |
E384410
|
entity |
| Predicate | genderNeutralSince |
P120782
|
FINISHED |
| Object | 1991 |
—
|
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: 1991 | Statement: [Crown Prince of Belgium, genderNeutralSince, 1991]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderNeutralSince Context triple: [Crown Prince of Belgium, genderNeutralSince, 1991]
-
A.
hasGenderNeutrality
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
B.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
C.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
genderSignificance
Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
| PDg | Predicate description generation | batch_69e174da2c2c819099ec46616798245a |
completed | April 16, 2026, 11:46 p.m. |
Created at: April 10, 2026, 4:50 a.m.