Triple
T19982189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reservation Road |
E493843
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Sean Curley
Sean Curley is an American actor best known for his role in the drama film "Reservation Road."
|
E1403725
|
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: Sean Curley | Statement: [Reservation Road, starring, Sean Curley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sean Curley Context triple: [Reservation Road, starring, Sean Curley]
-
A.
Joe Curran
Joe Curran is the main character in the 1970 American drama film "Joe," portrayed as a working-class man whose violent, reactionary views drive the movie’s central conflict.
-
B.
Matt O’Dowd
Matt O’Dowd is an astrophysicist and science communicator known for explaining complex cosmology and physics concepts to the public, particularly through online video series.
-
C.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
D.
Phil Heffernan
Phil Heffernan is an artist best known for creating the cover artwork for the novel "Candles Burning."
-
E.
Dan O'Brien
Dan O'Brien is a former American decathlete and Olympic gold medalist widely regarded as one of the greatest decathletes in history.
- 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: Sean Curley Triple: [Reservation Road, starring, Sean Curley]
Generated description
Sean Curley is an American actor best known for his role in the drama film "Reservation Road."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sean Curley Target entity description: Sean Curley is an American actor best known for his role in the drama film "Reservation Road."
-
A.
Joe Curran
Joe Curran is the main character in the 1970 American drama film "Joe," portrayed as a working-class man whose violent, reactionary views drive the movie’s central conflict.
-
B.
Matt O’Dowd
Matt O’Dowd is an astrophysicist and science communicator known for explaining complex cosmology and physics concepts to the public, particularly through online video series.
-
C.
Kevin O'Connell
Kevin O'Connell is an American football coach and former NFL quarterback who serves as the head coach of the Minnesota Vikings.
-
D.
Phil Heffernan
Phil Heffernan is an artist best known for creating the cover artwork for the novel "Candles Burning."
-
E.
Dan O'Brien
Dan O'Brien is a former American decathlete and Olympic gold medalist widely regarded as one of the greatest decathletes in history.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07fdddbf5c81908e194fb1d6c31e15 |
completed | May 16, 2026, 5:17 a.m. |
| NEDg | Description generation | batch_6a07fea23a988190832115346f8889ca |
completed | May 16, 2026, 5:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07ff4a59608190a17f30f60c879efc |
completed | May 16, 2026, 5:23 a.m. |
Created at: April 11, 2026, 3:28 p.m.