Triple
T4774590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princeton Tigers wrestling |
E106012
|
entity |
| Predicate | notableAlumnus |
P304
|
FINISHED |
| Object |
Matt Kolodzik
Matt Kolodzik is an American wrestler best known as a multiple-time All-American standout for Princeton University's wrestling program.
|
E477162
|
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: Matt Kolodzik | Statement: [Princeton Tigers wrestling, notableAlumnus, Matt Kolodzik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Kolodzik Context triple: [Princeton Tigers wrestling, notableAlumnus, Matt Kolodzik]
-
A.
Jeff Jagodzinski
Jeff Jagodzinski is an American football coach best known for his tenure as head coach at Boston College and his extensive experience as an offensive coach in both college football and the NFL.
-
B.
Mike Konopacki
Mike Konopacki is an American political cartoonist known for his labor- and social-justice-focused comics and graphic works.
-
C.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
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.
- 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: Matt Kolodzik Triple: [Princeton Tigers wrestling, notableAlumnus, Matt Kolodzik]
Generated description
Matt Kolodzik is an American wrestler best known as a multiple-time All-American standout for Princeton University's wrestling program.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Kolodzik Target entity description: Matt Kolodzik is an American wrestler best known as a multiple-time All-American standout for Princeton University's wrestling program.
-
A.
Jeff Jagodzinski
Jeff Jagodzinski is an American football coach best known for his tenure as head coach at Boston College and his extensive experience as an offensive coach in both college football and the NFL.
-
B.
Mike Konopacki
Mike Konopacki is an American political cartoonist known for his labor- and social-justice-focused comics and graphic works.
-
C.
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
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.
- 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_69bd43f3074c8190937e7b0a457fe9f1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6584481081908f1041a8827e0b42 |
completed | March 20, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be67c4460c81909d93ea5fcfa200e2 |
completed | March 21, 2026, 9:41 a.m. |
| NEDg | Description generation | batch_69be6a00d044819080b94fa01cc2a3a1 |
completed | March 21, 2026, 9:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be6a4e09e08190888eac716374e024 |
completed | March 21, 2026, 9:52 a.m. |
Created at: March 20, 2026, 1:21 p.m.