Triple
T15767733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glen Dale, West Virginia |
E382267
|
entity |
| Predicate | hasNotablePerson |
P304
|
FINISHED |
| Object |
Phil Neikro
Phil Niekro was a Hall of Fame Major League Baseball pitcher renowned for his mastery of the knuckleball, primarily with the Atlanta Braves.
|
E1174792
|
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: Phil Neikro | Statement: [Glen Dale, West Virginia, hasNotablePerson, Phil Neikro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phil Neikro Context triple: [Glen Dale, West Virginia, hasNotablePerson, Phil Neikro]
-
A.
Phil Neal
Phil Neal was the husband of American actress and singer Meredith MacRae.
-
B.
Mike McNeil
Mike McNeil is a software developer best known as the creator of the Sails.js Node.js web framework.
-
C.
Bill Neukom
Bill Neukom is an American lawyer and philanthropist best known as Microsoft’s former chief legal officer and a former managing general partner of the San Francisco Giants.
-
D.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
E.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
- 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: Phil Neikro Triple: [Glen Dale, West Virginia, hasNotablePerson, Phil Neikro]
Generated description
Phil Niekro was a Hall of Fame Major League Baseball pitcher renowned for his mastery of the knuckleball, primarily with the Atlanta Braves.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Phil Neikro Target entity description: Phil Niekro was a Hall of Fame Major League Baseball pitcher renowned for his mastery of the knuckleball, primarily with the Atlanta Braves.
-
A.
Phil Neal
Phil Neal was the husband of American actress and singer Meredith MacRae.
-
B.
Mike McNeil
Mike McNeil is a software developer best known as the creator of the Sails.js Node.js web framework.
-
C.
Bill Neukom
Bill Neukom is an American lawyer and philanthropist best known as Microsoft’s former chief legal officer and a former managing general partner of the San Francisco Giants.
-
D.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
E.
Mike Krieger
Mike Krieger is a Brazilian-American entrepreneur and software engineer best known as the co-founder and former CTO of the photo-sharing social media platform Instagram.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e051951bac8190a7d45f3612c6de72 |
completed | April 16, 2026, 3:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff877a67008190b05f879d05876fd3 |
completed | May 9, 2026, 7:14 p.m. |
| NEDg | Description generation | batch_69ff88430cb88190994039da4d3ce247 |
completed | May 9, 2026, 7:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff88aca9b08190ab2b687dd17d0845 |
completed | May 9, 2026, 7:19 p.m. |
Created at: April 10, 2026, 4:47 a.m.