Triple
T11951114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Bachelor Australia |
E284424
|
entity |
| Predicate | hasLeadGender |
P21355
|
FINISHED |
| Object | male lead (bachelor) |
—
|
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 lead (bachelor) | Statement: [The Bachelor Australia, hasLeadGender, male lead (bachelor)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeadGender Context triple: [The Bachelor Australia, hasLeadGender, male lead (bachelor)]
-
A.
hasLeadCharacterGender
chosen
Indicates that the primary or lead character in a work has a specified gender.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasGenderFocus
Indicates that something is specifically concerned with, oriented toward, or primarily addressing a particular gender or gender-related issues.
-
D.
hasHostGender
Indicates that an entity has or is associated with a specific gender of its host.
-
E.
hasGenderInterpretation
Indicates that an entity is associated with a particular interpretation or understanding of 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90364c2608190a3946c9595c71164 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.