Triple
T9339981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beckham |
E224738
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object |
Charles Beckham
Charles Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
|
E797708
|
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: Charles Beckham | Statement: [Beckham, usedBy, Charles Beckham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charles Beckham Context triple: [Beckham, usedBy, Charles Beckham]
-
A.
William Beckham
William Beckham is an individual associated with the use or ownership of an item or concept referred to as "Beckham."
-
B.
Thomas Beckham
Thomas Beckham is an individual associated with the use of the name or brand "Beckham," though specific public details about him are not widely documented.
-
C.
Robert Beckham
Robert Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
-
D.
Edward Beckham
Edward Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
-
E.
Malick Bowens
Malick Bowens was a Kenyan-born actor best known for his supporting role in the Academy Award–winning film "Out of Africa."
- 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: Charles Beckham Triple: [Beckham, usedBy, Charles Beckham]
Generated description
Charles Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charles Beckham Target entity description: Charles Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
-
A.
William Beckham
William Beckham is an individual associated with the use or ownership of an item or concept referred to as "Beckham."
-
B.
Thomas Beckham
Thomas Beckham is an individual associated with the use of the name or brand "Beckham," though specific public details about him are not widely documented.
-
C.
Robert Beckham
Robert Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
-
D.
Edward Beckham
Edward Beckham is an individual associated with the use or ownership of an item or concept referred to by the name Beckham.
-
E.
Malick Bowens
Malick Bowens was a Kenyan-born actor best known for his supporting role in the Academy Award–winning film "Out of Africa."
- 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_69ca84286fcc81909f6e7fd7a7e862a2 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd4bae2e2481909effc2dc89a642c5 |
completed | April 1, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1078635648190ac908fd6bd7228b0 |
completed | April 4, 2026, 12:43 p.m. |
| NEDg | Description generation | batch_69d1087d01b08190aa4d17241244c26c |
completed | April 4, 2026, 12:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1090215308190beda7c0115020f5b |
completed | April 4, 2026, 12:50 p.m. |
Created at: March 30, 2026, 7:40 p.m.