Triple
T26216817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | the cotter's children |
E655652
|
entity |
| Predicate | genderMix |
P51833
|
FINISHED |
| Object | sons and daughters |
—
|
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: sons and daughters | Statement: [the cotter's children, genderMix, sons and daughters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderMix Context triple: [the cotter's children, genderMix, sons and daughters]
-
A.
genderRatio
Indicates the proportional relationship between different genders within a given group or population.
-
B.
genderOfMembers
Indicates the gender or genders associated with the members of a group or organization.
-
C.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
-
D.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
E.
genderConfiguration
chosen
Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
- 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_69ee5b4a77e08190bfcb5f8ecdc55abd |
completed | April 26, 2026, 6:36 p.m. |
| NER | Named-entity recognition | batch_69f60d1b90a88190a77dcad664786c53 |
completed | May 2, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69f602d2ec748190ae95154f34c7878f |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 26, 2026, 8:54 p.m.