Triple
T12657684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Master of Disaster |
E302328
|
entity |
| Predicate | genderOfPersona |
P106045
|
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: [The Master of Disaster, genderOfPersona, male]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfPersona Context triple: [The Master of Disaster, genderOfPersona, male]
-
A.
genderOfPseudonym
Indicates the gender associated with a given pseudonym or pen name.
-
B.
genderPersonification
Indicates that a non-human entity is represented or treated as having a specific gender.
-
C.
genderTarget
Indicates that an action, message, or effect is specifically directed toward entities of a particular gender.
-
D.
genderConfiguration
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
-
E.
genderRule
Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b78ce8819091f15dd5013e6da5 |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d96179c7648190a05a13991d62bebb |
completed | April 10, 2026, 8:45 p.m. |
Created at: April 9, 2026, 5:19 p.m.