Triple
T17854615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O'Hare |
E445898
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
O'Haire
O'Haire is a variant spelling of the surname O'Hare, typically of Irish origin.
|
E1291870
|
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: O'Haire | Statement: [O'Hare, hasVariant, O'Haire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: O'Haire Context triple: [O'Hare, hasVariant, O'Haire]
-
A.
O'Hara
O'Hara is the surname of Scarlett O'Hara, the headstrong Southern heroine of Margaret Mitchell's novel "Gone with the Wind."
-
B.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
C.
Burke Nihill
Burke Nihill is an American sports executive who serves as the president and chief executive officer of the NFL’s Tennessee Titans.
-
D.
Leo Harrington
Leo Harrington is an American logician and mathematician known for his influential work in mathematical logic, particularly in recursion theory and set theory.
-
E.
John Hailey
John Hailey was a 19th-century Idaho pioneer, businessman, and politician whose contributions to the region’s early development led to the city of Hailey, Idaho being named in his honor.
- 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: O'Haire Triple: [O'Hare, hasVariant, O'Haire]
Generated description
O'Haire is a variant spelling of the surname O'Hare, typically of Irish origin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: O'Haire Target entity description: O'Haire is a variant spelling of the surname O'Hare, typically of Irish origin.
-
A.
O'Hara
O'Hara is the surname of Scarlett O'Hara, the headstrong Southern heroine of Margaret Mitchell's novel "Gone with the Wind."
-
B.
Hagey
Hagey is a surname most notably associated with Gerald Hagey, a prominent Canadian academic and founding president of the University of Waterloo.
-
C.
Burke Nihill
Burke Nihill is an American sports executive who serves as the president and chief executive officer of the NFL’s Tennessee Titans.
-
D.
Leo Harrington
Leo Harrington is an American logician and mathematician known for his influential work in mathematical logic, particularly in recursion theory and set theory.
-
E.
John Hailey
John Hailey was a 19th-century Idaho pioneer, businessman, and politician whose contributions to the region’s early development led to the city of Hailey, Idaho being named in his honor.
- F. None of above. chosen
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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4978af9b0819091780281344f5352 |
completed | April 19, 2026, 8:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a030c5ffcbc819086aec86f37c9c81c |
completed | May 12, 2026, 11:17 a.m. |
| NEDg | Description generation | batch_6a030d1204cc81908ceb5e5e1d203781 |
completed | May 12, 2026, 11:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a030d7916b48190a74cd5ebda25c0f3 |
completed | May 12, 2026, 11:22 a.m. |
Created at: April 10, 2026, 10:17 a.m.