Triple
T2900983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greyhound |
E62651
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object |
Mark Czyzewski
Mark Czyzewski is an editor known for his work on the film "Greyhound."
|
E319879
|
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 Czyzewski | Statement: [Greyhound, editedBy, Mark Czyzewski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Czyzewski Context triple: [Greyhound, editedBy, Mark Czyzewski]
-
A.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
B.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
-
C.
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.
-
D.
Andrew Bryniarski
Andrew Bryniarski is an American actor and former bodybuilder best known for playing imposing, physically intimidating characters in films such as The Texas Chainsaw Massacre (2003) and its prequel.
-
E.
Chris Malachowsky
Chris Malachowsky is an American engineer and entrepreneur best known as a co-founder of NVIDIA, a leading technology company in graphics processing and AI computing.
- 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 Czyzewski Triple: [Greyhound, editedBy, Mark Czyzewski]
Generated description
Mark Czyzewski is an editor known for his work on the film "Greyhound."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Czyzewski Target entity description: Mark Czyzewski is an editor known for his work on the film "Greyhound."
-
A.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
B.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
-
C.
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.
-
D.
Andrew Bryniarski
Andrew Bryniarski is an American actor and former bodybuilder best known for playing imposing, physically intimidating characters in films such as The Texas Chainsaw Massacre (2003) and its prequel.
-
E.
Chris Malachowsky
Chris Malachowsky is an American engineer and entrepreneur best known as a co-founder of NVIDIA, a leading technology company in graphics processing and AI computing.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0b261c081909b66b21520b4731b |
completed | March 7, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1de8b9378819084861d65dd2b9528 |
completed | March 11, 2026, 9:28 p.m. |
| NEDg | Description generation | batch_69b1df4acf2881908e969fe0512721bc |
completed | March 11, 2026, 9:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1dff55d5881909b14239a4232e617 |
completed | March 11, 2026, 9:34 p.m. |
Created at: March 6, 2026, 10:10 p.m.