Triple
T9434914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 11th Duke of Marlborough |
E227478
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | George |
E372352
|
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: George | Statement: [11th Duke of Marlborough, givenName, George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Context triple: [11th Duke of Marlborough, givenName, George]
-
A.
George
George is the heroic protagonist of the fantasy film "The Magic Sword," known for embarking on a perilous quest to rescue a princess from an evil sorcerer.
-
B.
George
George is the birth name of the legendary American baseball player Babe Ruth, one of the sport’s most iconic figures.
-
C.
George
chosen
George is a masculine given name of Greek origin meaning "farmer" or "earthworker," widely used in English-speaking and many other cultures.
-
D.
George
George is the given name of the Hero of Manila Bay, most famously associated with U.S. Admiral George Dewey, who led the decisive naval victory at the Battle of Manila Bay during the Spanish–American War.
-
E.
George
George is the given name of George Carnegie, 6th Earl of Northesk, a Scottish nobleman and naval officer in the Royal Navy.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e64109081908222f590928bc572 |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1104a002481909ca805893bac61c6 |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:50 p.m.