Triple
T85992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Maynard Keynes |
E1729
|
entity |
| Predicate | sexualOrientation |
P4324
|
FINISHED |
| Object | bisexual |
—
|
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: bisexual | Statement: [John Maynard Keynes, sexualOrientation, bisexual]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sexualOrientation Context triple: [John Maynard Keynes, sexualOrientation, bisexual]
-
A.
sexOrGender
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
B.
genderCategories
Indicates the classification of an entity into one or more gender-related categories or identities.
-
C.
hasNumberOfGenders
Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
-
D.
sexualDimorphism
Indicates differences in physical characteristics between males and females of a species that are systematically associated with their sex.
-
E.
orientation
Indicates the relative directional alignment or facing of one entity with respect to another or to a reference frame.
- 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a250e401288190ba12322c9c5f07c9 |
completed | Feb. 28, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69a24eb59e808190811c20518f39b1cc |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a250e2a80881909e5a653260e6f8e0 |
completed | Feb. 28, 2026, 2:20 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.