Triple
T17565260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ranon Ufgood |
E427794
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Mark Vandebrake
Mark Vandebrake is an actor known for playing the character Ranon Ufgood.
|
E1275286
|
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: Mark Vandebrake | Statement: [Ranon Ufgood, portrayedBy, Mark Vandebrake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Vandebrake Context triple: [Ranon Ufgood, portrayedBy, Mark Vandebrake]
-
A.
Tom Bruggere
Tom Bruggere is an American entrepreneur best known as the founding leader of Mentor Graphics, a pioneering company in electronic design automation.
-
B.
Tom Veldkamp
Tom Veldkamp is a Dutch academic and professor who serves as rector magnificus (chief academic officer) of the University of Twente.
-
C.
Thomas Rongen
Thomas Rongen is a Dutch-American soccer coach and former player known for his extensive coaching career in Major League Soccer and with various U.S. national youth teams.
-
D.
Tim Rogge
Tim Rogge is an individual notable enough to be recognized as a bearer of the surname Rogge, though specific widely known public details about him are not readily available.
-
E.
John Verhoogen
John Verhoogen was a prominent 20th-century geophysicist known for his influential work on the thermal and dynamic evolution of the Earth’s interior.
- 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: Mark Vandebrake Triple: [Ranon Ufgood, portrayedBy, Mark Vandebrake]
Generated description
Mark Vandebrake is an actor known for playing the character Ranon Ufgood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Vandebrake Target entity description: Mark Vandebrake is an actor known for playing the character Ranon Ufgood.
-
A.
Tom Bruggere
Tom Bruggere is an American entrepreneur best known as the founding leader of Mentor Graphics, a pioneering company in electronic design automation.
-
B.
Tom Veldkamp
Tom Veldkamp is a Dutch academic and professor who serves as rector magnificus (chief academic officer) of the University of Twente.
-
C.
Thomas Rongen
Thomas Rongen is a Dutch-American soccer coach and former player known for his extensive coaching career in Major League Soccer and with various U.S. national youth teams.
-
D.
Tim Rogge
Tim Rogge is an individual notable enough to be recognized as a bearer of the surname Rogge, though specific widely known public details about him are not readily available.
-
E.
John Verhoogen
John Verhoogen was a prominent 20th-century geophysicist known for his influential work on the thermal and dynamic evolution of the Earth’s interior.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4592ce42c8190a54a0a328c5e8ffc |
completed | April 19, 2026, 4:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01d29a14288190b5b665c5450b7c18 |
completed | May 11, 2026, 12:59 p.m. |
| NEDg | Description generation | batch_6a01d43430d481909b07d8ecc09a185f |
completed | May 11, 2026, 1:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01d4c609fc819089147744317d4be3 |
completed | May 11, 2026, 1:08 p.m. |
Created at: April 10, 2026, 5:50 a.m.