Triple
T2678124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patricia Bündchen |
E56507
|
entity |
| Predicate | twinType |
P42171
|
FINISHED |
| Object | fraternal twin |
—
|
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: fraternal twin | Statement: [Patricia Bündchen, twinType, fraternal twin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: twinType Context triple: [Patricia Bündchen, twinType, fraternal twin]
-
A.
hasTwin
Indicates that one entity is a twin of another, sharing the same birth event or time with a sibling.
-
B.
hasTwinChildren
Indicates that an entity is the parent of children who are twins.
-
C.
hasTwinTown
Indicates that two towns or cities are officially paired in a twinning relationship, typically for cultural, social, or economic exchange.
-
D.
twinCity
Indicates that two cities are officially recognized as twin (or sister) cities, typically signifying a formal partnership for cultural, economic, or social exchange.
-
E.
dualPair
Indicates that two entities form a dual pair, standing in a mathematically defined dual relationship where each is the dual counterpart of the other.
- 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abda2f7bf88190a1e3103dd014d871 |
completed | March 7, 2026, 7:56 a.m. |
| PD | Predicate disambiguation | batch_69abd81ab9d08190b72b6104c6dbc769 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abda2dc5788190b4b83cb9ed08266c |
completed | March 7, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:54 p.m.