Triple
T13757851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Fritz |
E330521
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
John
John Fritz was a prominent American mechanical engineer and steel industry pioneer known as the "Father of the U.S. Steel Industry."
|
E1059037
|
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 Fritz, givenName, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [John Fritz, givenName, John]
-
A.
John
John is the nickname of John Riggins, a former American football running back best known for his Hall of Fame career with the Washington Redskins in the NFL.
-
B.
John
John is the given name of John Arbuthnot Fisher, a prominent British admiral and naval reformer of the late 19th and early 20th centuries.
-
C.
John
John is the given name of the American composer John Luther Adams, known for his works inspired by nature and environmental themes.
-
D.
John
John is the given name of actor John Cho, a Korean American performer known for roles in the "Harold & Kumar" films and the "Star Trek" reboot series.
-
E.
John
John is the first name of the fictional character John Connor, the prophesied leader of the human resistance in the Terminator franchise.
- 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 Fritz, givenName, John]
Generated description
John Fritz was a prominent American mechanical engineer and steel industry pioneer known as the "Father of the U.S. Steel Industry."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Target entity description: John Fritz was a prominent American mechanical engineer and steel industry pioneer known as the "Father of the U.S. Steel Industry."
-
A.
John
John is the given name of John A. Roebling II, an American civil engineer and philanthropist from the prominent Roebling family associated with major bridge construction.
-
B.
John
John is the given name of John Warne Gates, the American industrialist and financier known for promoting barbed wire and early oil ventures.
-
C.
John
John is the given name of John Henry Patterson, an American industrialist and founder of the National Cash Register Company.
-
D.
John
John is the given name of John L. Lewis, the influential American labor leader who headed the United Mine Workers of America and helped shape the modern labor movement.
-
E.
John
John is the given name of John Frank Stevens, the American civil engineer best known for his pivotal role in the construction of the Panama Canal.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de022286b481908f8a801042743512 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a845a29c81908096a785f5af5521 |
completed | May 3, 2026, 7:55 p.m. |
| NEDg | Description generation | batch_69f7a8f6833881908bcca35d7d01596a |
completed | May 3, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a9c3c6548190802e1163c9c35b67 |
completed | May 3, 2026, 8:02 p.m. |
Created at: April 9, 2026, 10:09 p.m.