Triple
T4924809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston Aeros |
E110551
|
entity |
| Predicate | notablePlayer |
P304
|
FINISHED |
| Object |
Don McLeod
Don McLeod was a Canadian professional ice hockey goaltender best known for his standout play in the World Hockey Association during the 1970s.
|
E482276
|
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: Don McLeod | Statement: [Houston Aeros, notablePlayer, Don McLeod]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don McLeod Context triple: [Houston Aeros, notablePlayer, Don McLeod]
-
A.
Andrew McElfresh
Andrew McElfresh is an American comedy writer and screenwriter known for his work on films such as "White Chicks."
-
B.
Eric McLeod
Eric McLeod is a film producer known for his work on major Hollywood action and genre movies, including the monster crossover blockbuster "Godzilla vs. Kong."
-
C.
Donald McLeod
Donald McLeod was a Loyalist officer in the American Revolutionary War who led British-aligned forces at the Battle of Moore’s Creek Bridge in 1776.
-
D.
Mike MacLean
Mike MacLean is a screenwriter best known for his work on the cult sci-fi horror film "Sharktopus" and other genre projects.
-
E.
Thomas Coulter
Thomas Coulter was a 19th-century Irish physician, botanist, and explorer known for his plant collections in Mexico and the southwestern United States.
- 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: Don McLeod Triple: [Houston Aeros, notablePlayer, Don McLeod]
Generated description
Don McLeod was a Canadian professional ice hockey goaltender best known for his standout play in the World Hockey Association during the 1970s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Don McLeod Target entity description: Don McLeod was a Canadian professional ice hockey goaltender best known for his standout play in the World Hockey Association during the 1970s.
-
A.
Andrew McElfresh
Andrew McElfresh is an American comedy writer and screenwriter known for his work on films such as "White Chicks."
-
B.
Eric McLeod
Eric McLeod is a film producer known for his work on major Hollywood action and genre movies, including the monster crossover blockbuster "Godzilla vs. Kong."
-
C.
Donald McLeod
Donald McLeod was a Loyalist officer in the American Revolutionary War who led British-aligned forces at the Battle of Moore’s Creek Bridge in 1776.
-
D.
Mike MacLean
Mike MacLean is a screenwriter best known for his work on the cult sci-fi horror film "Sharktopus" and other genre projects.
-
E.
Thomas Coulter
Thomas Coulter was a 19th-century Irish physician, botanist, and explorer known for his plant collections in Mexico and the southwestern United States.
- 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_69bd4413f9908190afcff44d7929cc4c |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ffeb86c8190a2fabe1ae1d54118 |
completed | March 20, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81c2cb288190b0a603992c08235c |
completed | March 21, 2026, 11:32 a.m. |
| NEDg | Description generation | batch_69be83318d948190a5fb3d7cf8cff497 |
completed | March 21, 2026, 11:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be839b52bc8190aeddc775913254b4 |
completed | March 21, 2026, 11:40 a.m. |
Created at: March 20, 2026, 1:30 p.m.