Triple
T13688138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theron |
E328186
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Johan Theron
Johan Theron is a South African former professional tennis player who competed primarily on the ITF Futures and ATP Challenger circuits.
|
E1057985
|
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: Johan Theron | Statement: [Theron, hasNotableBearer, Johan Theron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johan Theron Context triple: [Theron, hasNotableBearer, Johan Theron]
-
A.
Rudi Theron
Rudi Theron is a person notable enough to be recognized as a namesake or prominent individual associated with the surname Theron.
-
B.
Alan Durband
Alan Durband was a British teacher, writer, and influential drama educator from Liverpool, known for his popular guides to Shakespeare and his impact on English education.
-
C.
Adriaan Hendrik Stander
Adriaan Hendrik Stander was a notable historical figure after whom the South African town of Standerton was named.
-
D.
Hennie
Hennie is a Norwegian surname most notably associated with actor and director Aksel Hennie.
-
E.
Daniel Henney
Daniel Henney is an American actor and model known for his roles in films and television series such as "Big Hero 6," "Criminal Minds," and "The Wheel of Time."
- 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: Johan Theron Triple: [Theron, hasNotableBearer, Johan Theron]
Generated description
Johan Theron is a South African former professional tennis player who competed primarily on the ITF Futures and ATP Challenger circuits.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Johan Theron Target entity description: Johan Theron is a South African former professional tennis player who competed primarily on the ITF Futures and ATP Challenger circuits.
-
A.
Rudi Theron
Rudi Theron is a person notable enough to be recognized as a namesake or prominent individual associated with the surname Theron.
-
B.
Alan Durband
Alan Durband was a British teacher, writer, and influential drama educator from Liverpool, known for his popular guides to Shakespeare and his impact on English education.
-
C.
Adriaan Hendrik Stander
Adriaan Hendrik Stander was a notable historical figure after whom the South African town of Standerton was named.
-
D.
Hennie
Hennie is a Norwegian surname most notably associated with actor and director Aksel Hennie.
-
E.
Daniel Henney
Daniel Henney is an American actor and model known for his roles in films and television series such as "Big Hero 6," "Criminal Minds," and "The Wheel of Time."
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc670968881908e2b4fdf656c7285 |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d4c52fc8190a93d05c24a8d1513 |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f7a15f3c908190be380355972def6e |
completed | May 3, 2026, 7:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a2234390819093814fd435f9c42c |
completed | May 3, 2026, 7:29 p.m. |
Created at: April 9, 2026, 9:53 p.m.