Triple
T13634715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabriela Bündchen |
E325817
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bündchen |
E55548
|
NE 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: Bündchen | Statement: [Gabriela Bündchen, familyName, Bündchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bündchen Context triple: [Gabriela Bündchen, familyName, Bündchen]
-
A.
Bündchen
chosen
Bündchen is the German-origin surname most famously borne by Brazilian supermodel Gisele Bündchen.
-
B.
Pom-Pom
Pom-Pom is the pampered white palace cat in Disney’s Cinderella universe, known for antagonizing the mice, especially Jaq and Gus.
-
C.
Bebek
Bebek is an upscale seaside neighborhood on Istanbul’s Bosphorus shore, known for its scenic views, cafes, and vibrant social life.
-
D.
Bibby
Bibby is a surname most prominently associated with former NBA point guard Mike Bibby.
-
E.
Biskinik
Biskinik is the official newspaper of the Choctaw Nation, providing news, cultural information, and community updates for Choctaw citizens.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8076beddc8190a53156f5bea77f5e |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc5a490508190924ac40f1dd519d6 |
completed | April 12, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78aef6fd08190b209a94b9ddd024c |
completed | May 3, 2026, 5:50 p.m. |
Created at: April 9, 2026, 9:51 p.m.