Triple
T17887514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ultra-Humanite |
E447234
|
entity |
| Predicate | genderInOriginalBody |
P72
|
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: [Ultra-Humanite, genderInOriginalBody, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderInOriginalBody Context triple: [Ultra-Humanite, genderInOriginalBody, male]
-
A.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
B.
genderCustom
Indicates that an entity has a user-specified or non-standard gender designation beyond predefined gender categories.
-
C.
sexOrGender
chosen
Indicates that one entity has a specified biological sex or socially constructed gender identity.
-
D.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
E.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c14c4348190bb77712d9eae1d51 |
completed | April 19, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:18 a.m.