Triple
T13186080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hager |
E313855
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Asa Hager
Asa Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
|
E1106196
|
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: Asa Hager | Statement: [Hager, hasNotableBearer, Asa Hager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asa Hager Context triple: [Hager, hasNotableBearer, Asa Hager]
-
A.
Asa Biggs
Asa Biggs was a 19th-century American politician from North Carolina who served as a U.S. Representative, U.S. Senator, and later a federal judge.
-
B.
Asa Danforth Jr.
Asa Danforth Jr. was an early American-born land developer and road builder active in Upper Canada in the late 18th and early 19th centuries.
-
C.
Asa Hawks
Asa Hawks is a blind street preacher and con man in Flannery O’Connor’s novel "Wise Blood," symbolizing religious hypocrisy and spiritual emptiness.
-
D.
Hugo Barnstead
Hugo Barnstead is a central character in the 1941 romantic comedy film "The Strawberry Blonde," portrayed as a brash, ambitious rival to the more easygoing protagonist.
-
E.
John Swett
John Swett was a prominent 19th-century American educator and reformer often regarded as the "father of the California public school system."
- 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: Asa Hager Triple: [Hager, hasNotableBearer, Asa Hager]
Generated description
Asa Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Asa Hager Target entity description: Asa Hager is an individual notable enough to be recognized as a prominent bearer of the surname Hager.
-
A.
Asa Biggs
Asa Biggs was a 19th-century American politician from North Carolina who served as a U.S. Representative, U.S. Senator, and later a federal judge.
-
B.
Asa Danforth Jr.
Asa Danforth Jr. was an early American-born land developer and road builder active in Upper Canada in the late 18th and early 19th centuries.
-
C.
Asa Hawks
Asa Hawks is a blind street preacher and con man in Flannery O’Connor’s novel "Wise Blood," symbolizing religious hypocrisy and spiritual emptiness.
-
D.
Hugo Barnstead
Hugo Barnstead is a central character in the 1941 romantic comedy film "The Strawberry Blonde," portrayed as a brash, ambitious rival to the more easygoing protagonist.
-
E.
John Swett
John Swett was a prominent 19th-century American educator and reformer often regarded as the "father of the California public school system."
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4b663c8190b0b18f0785f7b57d |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8a9c41908190b789765861bd9924 |
completed | May 8, 2026, 7:02 a.m. |
| NEDg | Description generation | batch_69fd8bd70488819083f40c38575f3071 |
completed | May 8, 2026, 7:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8d4f2e848190a3c4c423c0ffed50 |
completed | May 8, 2026, 7:14 a.m. |
Created at: April 9, 2026, 9:15 p.m.