Triple
T16956230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | György |
E411309
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | George |
unclear NED1
|
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: [György, relatedName, George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Context triple: [György, relatedName, George]
-
A.
George
George is a common English surname of likely Greek and Latin origin, associated with numerous notable historical and contemporary figures.
-
B.
George
George is the given name of George Murray, 6th Duke of Atholl, a Scottish peer and nobleman of the 19th century.
-
C.
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.
-
D.
George
George is the given first name of the American gangster Bugs Moran, a prominent Prohibition-era mobster in Chicago.
-
E.
George
George is the given name of George North, 3rd Earl of Guilford, a British peer from the late 18th and early 19th centuries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d01c7b408190b5a2b6c62b050b76 |
completed | April 18, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00dc06e6b481908d8032f5772762c9 |
completed | May 10, 2026, 7:27 p.m. |
Created at: April 10, 2026, 5:31 a.m.