Triple
T12226065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bündchen family |
E291354
|
entity |
| Predicate | hasNotableFemaleMembers |
P82686
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bündchen family, hasNotableFemaleMembers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFemaleMembers Context triple: [Bündchen family, hasNotableFemaleMembers, yes]
-
A.
notableFemaleMember
chosen
Indicates that an entity has a female member who is particularly prominent, distinguished, or noteworthy within that entity.
-
B.
hasNotableMember
Indicates that a group, organization, or collection includes at least one member who is distinguished or noteworthy in some significant way.
-
C.
hasFemaleVocalist
Indicates that the subject entity features or includes at least one female vocalist as a performer.
-
D.
hadWomenOrganization
Indicates that an entity was associated with or involved in an organization focused on women or women’s issues.
-
E.
hasFemaleLeader
Indicates that the subject entity is led or governed by a woman in a primary leadership role.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:51 p.m.