Triple
T4713885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Eager Howard |
E104584
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
John
John is the given name of John Eager Howard, an American Revolutionary War officer and early political leader from Maryland.
|
E466067
|
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: John | Statement: [John Eager Howard, givenName, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [John Eager Howard, givenName, John]
-
A.
John
John is the given first name of the legendary American professional golfer Byron Nelson, one of the sport’s early great champions.
-
B.
John
John is the husband of Martha Rainsborough.
-
C.
John
John is the given name of American actor John Goodman, renowned for his roles in film, television, and theater.
-
D.
John
John is the given name of John F. Fitzgerald, an American politician who served as mayor of Boston and was the maternal grandfather of President John F. Kennedy.
-
E.
John
John Guillermin was a British film director and producer best known for directing large-scale adventure and disaster films such as "The Towering Inferno" and the 1976 remake of "King Kong."
- 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: John Triple: [John Eager Howard, givenName, John]
Generated description
John is the given name of John Eager Howard, an American Revolutionary War officer and early political leader from Maryland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Target entity description: John is the given name of John Eager Howard, an American Revolutionary War officer and early political leader from Maryland.
-
A.
John
John is the given name of John Hancock, a prominent American statesman and patriot best known for his large signature on the United States Declaration of Independence.
-
B.
John
John is the first name of John Witherspoon, an American Founding Father, Presbyterian minister, and president of the College of New Jersey (now Princeton University).
-
C.
John
John is the given name of John J. Pershing, the famed American general who led the American Expeditionary Forces in World War I.
-
D.
John
John is the given name of John Jay, one of the Founding Fathers of the United States and the first Chief Justice of the U.S. Supreme Court.
-
E.
John
John is the given name of John Alexander Logan, a prominent 19th-century American Civil War general and influential politician.
- 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_69bd43ec4a348190bc41afae43375e71 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6407659481908bcdc063d7b3da00 |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be39e6428c81909be9bdb314993b1e |
completed | March 21, 2026, 6:25 a.m. |
| NEDg | Description generation | batch_69be3c02014c81908a6f3ed676e5505c |
completed | March 21, 2026, 6:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be3cd64d0c8190b007e9f027185225 |
completed | March 21, 2026, 6:38 a.m. |
Created at: March 20, 2026, 1:18 p.m.