Triple
T14858156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kansas City Royals Hall of Fame |
E349413
|
entity |
| Predicate | hasInductee |
P1750
|
FINISHED |
| Object |
Mark Gubicza
Mark Gubicza is a former Major League Baseball pitcher best known for his long and successful career with the Kansas City Royals, including two All-Star selections and a key role on their 1985 World Series championship team.
|
E1124143
|
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: Mark Gubicza | Statement: [Kansas City Royals Hall of Fame, hasInductee, Mark Gubicza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Gubicza Context triple: [Kansas City Royals Hall of Fame, hasInductee, Mark Gubicza]
-
A.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
B.
Michael Kuzak
Michael Kuzak is a central attorney character on the television legal drama "L.A. Law," known for his idealism and high-profile courtroom battles.
-
C.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
D.
Marc Sirkin
Marc Sirkin is a local political leader who serves as the mayor of Blue Ash, Ohio.
-
E.
George Kralovansky
George Kralovansky is a television producer best known for his executive production work on the live law-enforcement reality series "Live PD."
- 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: Mark Gubicza Triple: [Kansas City Royals Hall of Fame, hasInductee, Mark Gubicza]
Generated description
Mark Gubicza is a former Major League Baseball pitcher best known for his long and successful career with the Kansas City Royals, including two All-Star selections and a key role on their 1985 World Series championship team.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Gubicza Target entity description: Mark Gubicza is a former Major League Baseball pitcher best known for his long and successful career with the Kansas City Royals, including two All-Star selections and a key role on their 1985 World Series championship team.
-
A.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
B.
Michael Kuzak
Michael Kuzak is a central attorney character on the television legal drama "L.A. Law," known for his idealism and high-profile courtroom battles.
-
C.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
D.
Marc Sirkin
Marc Sirkin is a local political leader who serves as the mayor of Blue Ash, Ohio.
-
E.
George Kralovansky
George Kralovansky is a television producer best known for his executive production work on the live law-enforcement reality series "Live PD."
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded44598e48190b759a05ed2d9ecaf |
completed | April 14, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe650a43bc8190b836fe690d2a3c71 |
completed | May 8, 2026, 10:34 p.m. |
| NEDg | Description generation | batch_69fe66a5f3a88190827c6c9247323153 |
completed | May 8, 2026, 10:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6736ff34819098524e4401a414aa |
completed | May 8, 2026, 10:44 p.m. |
Created at: April 10, 2026, 1:54 a.m.