Triple
T16369278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christian Smith |
E397520
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object |
Smith
Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
|
E30542
|
NE FINISHED |
How this triple was built (4 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: Smith | Statement: [Christian Smith, hasSurname, Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Smith Context triple: [Christian Smith, hasSurname, Smith]
-
A.
John
John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
-
B.
John
John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
-
C.
John
John Brabourne was a British film and television producer and peer, known for producing works such as the 1979 adaptation of "Murder on the Orient Express."
-
D.
John
John is the given name of John Bowen, a British novelist and playwright known for his crime and speculative fiction.
-
E.
John
John is the given name of John Boyle O'Reilly, a 19th-century Irish-born poet, journalist, and civil rights activist who became influential in the United States.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Smith Triple: [Christian Smith, hasSurname, Smith]
Generated description
Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Smith Target entity description: Smith is a common English-language surname borne by numerous notable individuals across diverse fields such as politics, sports, science, and the arts.
-
A.
Smith
chosen
Smith is a common English surname borne by numerous notable individuals across diverse fields such as politics, arts, sports, and academia.
-
B.
Jones
Jones is a common English-language surname borne by numerous notable individuals across fields such as entertainment, sports, politics, and science.
-
C.
John
John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
-
D.
John
John Cicero was a late 15th-century Elector of Brandenburg from the House of Hohenzollern who helped consolidate the territory’s political and administrative structures within the Holy Roman Empire.
-
E.
John
John Brabourne was a British film and television producer and peer, known for producing works such as the 1979 adaptation of "Murder on the Orient Express."
- F. None of above.
Provenance (5 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_69d87f2778dc8190aa95c7572db127e6 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2ff4021e88190ad093bab74cf82a4 |
completed | April 18, 2026, 3:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002dc29f088190ba5d69ff3c12a251 |
completed | May 10, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_6a002ec2fd948190878af958d0b90ce6 |
completed | May 10, 2026, 7:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00312a4fc48190b6bd6ad9db71bb4d |
completed | May 10, 2026, 7:18 a.m. |
Created at: April 10, 2026, 5:08 a.m.