Triple
T20713858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chargers Hall of Fame |
E509116
|
entity |
| Predicate | notableInductee |
P7102
|
FINISHED |
| Object |
Paul Lowe
Paul Lowe is a former American Football League star running back best known for his standout career with the San Diego Chargers.
|
E1447376
|
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: Paul Lowe | Statement: [Chargers Hall of Fame, notableInductee, Paul Lowe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Lowe Context triple: [Chargers Hall of Fame, notableInductee, Paul Lowe]
-
A.
Paul Hunter
Paul Hunter is an acclaimed American music video director known for his visually innovative work with major artists across hip-hop, R&B, and pop.
-
B.
Paul Hunter
Paul Hunter is a film editor known for his work on animated feature films such as "The Nut Job."
-
C.
Paul Hunter
Paul Hunter was an English professional snooker player renowned for his flair, charisma, and multiple major ranking titles before his career was cut short by his early death.
-
D.
Paul Groth
Paul Groth is a computer scientist known for his work in knowledge representation, semantic web technologies, and data provenance.
-
E.
Tom Budge
Tom Budge is an Australian actor known for his character roles in film and television, including appearances in projects like "The Proposition" and "Gallipoli."
- 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: Paul Lowe Triple: [Chargers Hall of Fame, notableInductee, Paul Lowe]
Generated description
Paul Lowe is a former American Football League star running back best known for his standout career with the San Diego Chargers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paul Lowe Target entity description: Paul Lowe is a former American Football League star running back best known for his standout career with the San Diego Chargers.
-
A.
Paul Hunter
Paul Hunter is an acclaimed American music video director known for his visually innovative work with major artists across hip-hop, R&B, and pop.
-
B.
Paul Hunter
Paul Hunter is a film editor known for his work on animated feature films such as "The Nut Job."
-
C.
Paul Hunter
Paul Hunter was an English professional snooker player renowned for his flair, charisma, and multiple major ranking titles before his career was cut short by his early death.
-
D.
Paul Groth
Paul Groth is a computer scientist known for his work in knowledge representation, semantic web technologies, and data provenance.
-
E.
Tom Budge
Tom Budge is an Australian actor known for his character roles in film and television, including appearances in projects like "The Proposition" and "Gallipoli."
- 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_69e0b4c40ad88190b81f77695366d328 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d001788190a524a17347f9de67 |
completed | April 21, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08e04f58648190acf2cdc03ada05a4 |
completed | May 16, 2026, 9:23 p.m. |
| NEDg | Description generation | batch_6a08e15d93ec8190bbe859fb42a4a5ac |
completed | May 16, 2026, 9:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08e25d8cb08190ba75b792aac192ab |
completed | May 16, 2026, 9:32 p.m. |
Created at: April 16, 2026, 12:15 p.m.