Triple
T4642860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brown |
E101694
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Braun |
E67478
|
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: Braun | Statement: [Brown, hasVariant, Braun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Braun Context triple: [Brown, hasVariant, Braun]
-
A.
Braun
chosen
Braun is a German surname most infamously associated with Eva Braun, the longtime companion and brief wife of Adolf Hitler.
-
B.
Braun-Pivet
Braun-Pivet is the surname of Yaël Braun-Pivet, a prominent French politician who has served as President of the National Assembly.
-
C.
Blomberg
Blomberg is a small town in the Lippe district of North Rhine-Westphalia, Germany, known as the birthplace of former German chancellor Gerhard Schröder.
-
D.
Gillette
Gillette is a globally recognized American brand best known for its razors and shaving products.
-
E.
Brinkman
Brinkman is a surname of Germanic origin borne by various notable individuals across fields such as sports, politics, and the arts.
- 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a93047c8190990c94fd5a57c867 |
completed | March 20, 2026, 2:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfad80f14819097e022c9d9da17eb |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:14 p.m.