Triple
T1746306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1998 NHL All-Star Game |
E38342
|
entity |
| Predicate | linesman |
P32073
|
FINISHED |
| Object |
Mark Pare
Mark Pare is a professional ice hockey linesman who officiated at the highest levels of the sport, including the NHL All-Star Game.
|
E197722
|
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 Pare | Statement: [1998 NHL All-Star Game, linesman, Mark Pare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Pare Context triple: [1998 NHL All-Star Game, linesman, Mark Pare]
-
A.
Mark Parsons
Mark Parsons is an English football manager best known for his successful tenure leading the Portland Thorns FC in the National Women's Soccer League.
-
B.
Mark Herron
Mark Herron was an American actor best known for being the fourth husband of legendary entertainer Judy Garland.
-
C.
Chris Parnell
Chris Parnell is an American actor and comedian best known for his work on "Saturday Night Live" and roles in series like "30 Rock," "Archer," and "Rick and Morty."
-
D.
Marc Eversley
Marc Eversley is a Canadian basketball executive known for serving as the general manager of the NBA’s Chicago Bulls.
-
E.
Keith Fraase
Keith Fraase is a film editor best known for his work on the movie "Chappaquiddick."
- 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 Pare Triple: [1998 NHL All-Star Game, linesman, Mark Pare]
Generated description
Mark Pare is a professional ice hockey linesman who officiated at the highest levels of the sport, including the NHL All-Star Game.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Pare Target entity description: Mark Pare is a professional ice hockey linesman who officiated at the highest levels of the sport, including the NHL All-Star Game.
-
A.
Mark Parsons
Mark Parsons is an English football manager best known for his successful tenure leading the Portland Thorns FC in the National Women's Soccer League.
-
B.
Mark Herron
Mark Herron was an American actor best known for being the fourth husband of legendary entertainer Judy Garland.
-
C.
Chris Parnell
Chris Parnell is an American actor and comedian best known for his work on "Saturday Night Live" and roles in series like "30 Rock," "Archer," and "Rick and Morty."
-
D.
Marc Eversley
Marc Eversley is a Canadian basketball executive known for serving as the general manager of the NBA’s Chicago Bulls.
-
E.
Keith Fraase
Keith Fraase is a film editor best known for his work on the movie "Chappaquiddick."
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abaffd9b68819084f6c4d5e1aace1e |
completed | March 7, 2026, 4:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0de0ff08190bb7758e3ba32de80 |
completed | March 8, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69ada524a234819082f94430da4e802d |
completed | March 8, 2026, 4:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada5bc32ac8190a921410bdfa465fa |
completed | March 8, 2026, 4:37 p.m. |
Created at: March 4, 2026, 7:31 p.m.