Triple
T13936440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord of Hautdesert |
E335132
|
entity |
| Predicate | hasTitleHolderGender |
P85981
|
FINISHED |
| Object | male |
—
|
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: male | Statement: [Lord of Hautdesert, hasTitleHolderGender, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTitleHolderGender Context triple: [Lord of Hautdesert, hasTitleHolderGender, male]
-
A.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
-
B.
titleHolderSex
chosen
Indicates the biological or identified sex of the person who holds a particular title.
-
C.
hasTitleHolder
Indicates that one entity is the current or designated holder of a specific title, position, or honor associated with another entity.
-
D.
genderOfFirstHolder
Indicates that the relationship specifies the gender of the first entity that holds or possesses something in the described context.
-
E.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf42878819085146670d7b92605 |
completed | April 14, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69dbc873052c8190b33ff7f7c5a4e7ee |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:17 p.m.