Triple
T12997440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Van Der Beek |
E322076
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Van Der Beek
Van Der Beek is a Dutch-origin surname most prominently associated with American actor James Van Der Beek.
|
E1020151
|
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: Van Der Beek | Statement: [James Van Der Beek, familyName, Van Der Beek]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Van Der Beek Context triple: [James Van Der Beek, familyName, Van Der Beek]
-
A.
Matt Bomer
Matt Bomer is an American actor known for his roles in the TV series "White Collar" and films such as "Magic Mike" and "The Normal Heart."
-
B.
Justin Hartley
Justin Hartley is an American actor best known for his television roles in series such as "This Is Us," "Smallville," and "The Young and the Restless."
-
C.
Tyler Hoechlin
Tyler Hoechlin is an American actor best known for roles such as Derek Hale on "Teen Wolf" and Superman/Clark Kent in the Arrowverse series.
-
D.
Colin Donnell
Colin Donnell is an American actor best known for his television roles in series such as "Arrow" and "Chicago Med," as well as his work on Broadway.
-
E.
Vincent Kartheiser
Vincent Kartheiser is an American actor best known for his role as ambitious ad executive Pete Campbell on the television series "Mad Men."
- 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: Van Der Beek Triple: [James Van Der Beek, familyName, Van Der Beek]
Generated description
Van Der Beek is a Dutch-origin surname most prominently associated with American actor James Van Der Beek.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Van Der Beek Target entity description: Van Der Beek is a Dutch-origin surname most prominently associated with American actor James Van Der Beek.
-
A.
Matt Bomer
Matt Bomer is an American actor known for his roles in the TV series "White Collar" and films such as "Magic Mike" and "The Normal Heart."
-
B.
Justin Hartley
Justin Hartley is an American actor best known for his television roles in series such as "This Is Us," "Smallville," and "The Young and the Restless."
-
C.
Tyler Hoechlin
Tyler Hoechlin is an American actor best known for roles such as Derek Hale on "Teen Wolf" and Superman/Clark Kent in the Arrowverse series.
-
D.
Colin Donnell
Colin Donnell is an American actor best known for his television roles in series such as "Arrow" and "Chicago Med," as well as his work on Broadway.
-
E.
Vincent Kartheiser
Vincent Kartheiser is an American actor best known for his role as ambitious ad executive Pete Campbell on the television series "Mad Men."
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7980288190a9fe629a8cc76a52 |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d5f6c4e081909f035260462015b1 |
completed | May 3, 2026, 4:58 a.m. |
| NEDg | Description generation | batch_69f6d9cddf548190b43fe4edd7332957 |
completed | May 3, 2026, 5:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6da5b7098819092feb7064d5e7755 |
completed | May 3, 2026, 5:17 a.m. |
Created at: April 9, 2026, 8:45 p.m.