Triple
T10039680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clemons |
E205261
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object |
Joseph Clemons
Joseph Clemons is a person most likely known in this context as the primary user or bearer of the name "Clemons."
|
E843705
|
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: Joseph Clemons | Statement: [Clemons, usedBy, Joseph Clemons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Clemons Context triple: [Clemons, usedBy, Joseph Clemons]
-
A.
Leon Barmore
Leon Barmore is a legendary women's college basketball coach best known for leading the Louisiana Tech Lady Techsters to national prominence and multiple NCAA championships.
-
B.
John Clemons
John Clemons is an individual whose specific public notability or profession is not clearly identifiable from the given information.
-
C.
Eugene Worley
Eugene Worley was an American jurist who served as a prominent judge on the United States Court of Customs and Patent Appeals.
-
D.
Frank Melton
Frank Melton was an American actor best known for his role in the 1940 musical film "Second Chorus."
-
E.
Clem Burke
Clem Burke is an American drummer best known for his long-time work with the rock band Blondie.
- 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: Joseph Clemons Triple: [Clemons, usedBy, Joseph Clemons]
Generated description
Joseph Clemons is a person most likely known in this context as the primary user or bearer of the name "Clemons."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joseph Clemons Target entity description: Joseph Clemons is a person most likely known in this context as the primary user or bearer of the name "Clemons."
-
A.
Leon Barmore
Leon Barmore is a legendary women's college basketball coach best known for leading the Louisiana Tech Lady Techsters to national prominence and multiple NCAA championships.
-
B.
John Clemons
John Clemons is an individual whose specific public notability or profession is not clearly identifiable from the given information.
-
C.
Eugene Worley
Eugene Worley was an American jurist who served as a prominent judge on the United States Court of Customs and Patent Appeals.
-
D.
Frank Melton
Frank Melton was an American actor best known for his role in the 1940 musical film "Second Chorus."
-
E.
Clem Burke
Clem Burke is an American drummer best known for his long-time work with the rock band Blondie.
- 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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcee186708190bc9fecd637b4f7e6 |
completed | April 2, 2026, 2:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e559a1608190903e9b2dff12bb00 |
completed | April 5, 2026, 10:42 p.m. |
| NEDg | Description generation | batch_69d2e6f0aa988190aa9a866afcc2a1a2 |
completed | April 5, 2026, 10:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2e78384f48190abb7bdd7fcadcd9a |
completed | April 5, 2026, 10:51 p.m. |
Created at: March 30, 2026, 8:55 p.m.