Triple
T10803556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Eugene Uhlenbeck |
E254904
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | George |
E685754
|
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: [George Eugene Uhlenbeck, givenName, George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Context triple: [George Eugene Uhlenbeck, 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 a common English surname of likely Greek and Latin origin, associated with numerous notable historical and contemporary figures.
-
C.
George
George is the given name of George Murray, 6th Duke of Atholl, a Scottish peer and nobleman of the 19th century.
-
D.
George
chosen
George is the given name of George de Hevesy, the Hungarian radiochemist and Nobel laureate known for pioneering the use of radioactive tracers in studying chemical processes.
-
E.
George
George is a supporting character in the romantic comedy film "27 Dresses," serving as a colleague and love interest within the story’s central wedding-planning world.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7336feff88190b638b7d62d34da0e |
completed | April 9, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de55bb98ec8190914031643c1c7a97 |
completed | April 14, 2026, 2:56 p.m. |
Created at: April 8, 2026, 9:18 p.m.