Triple
T12875464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew Salinger |
E307953
|
entity |
| Predicate | hasActedIn |
P15620
|
FINISHED |
| Object |
Second Chances
Second Chances is a film featuring actor Matthew Salinger in its cast.
|
E1007629
|
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: Second Chances | Statement: [Matthew Salinger, hasActedIn, Second Chances]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Second Chances Context triple: [Matthew Salinger, hasActedIn, Second Chances]
-
A.
Second Chances
Second Chances is a book authored by Patricia Southall that reflects her personal journey and insights on faith, resilience, and starting over.
-
B.
A Second Chance
A Second Chance is a 2014 Danish drama film directed by Susanne Bier that explores moral dilemmas and the limits of justice through the story of a police officer facing a harrowing personal crisis.
-
C.
2nd Chance
2nd Chance is the second novel in James Patterson’s Women’s Murder Club crime thriller series, following a team of women professionals who work together to solve a string of brutal murders.
-
D.
Second Chance
Second Chance is a 1953 American film noir crime drama notable for its early use of 3D technology and direction by Rudolph Maté.
-
E.
One Chance
One Chance is a 2013 British biographical comedy-drama film about opera singer Paul Potts, directed by David Frankel.
- 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: Second Chances Triple: [Matthew Salinger, hasActedIn, Second Chances]
Generated description
Second Chances is a film featuring actor Matthew Salinger in its cast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Second Chances Target entity description: Second Chances is a film featuring actor Matthew Salinger in its cast.
-
A.
Second Chances
Second Chances is a book authored by Patricia Southall that reflects her personal journey and insights on faith, resilience, and starting over.
-
B.
A Second Chance
A Second Chance is a 2014 Danish drama film directed by Susanne Bier that explores moral dilemmas and the limits of justice through the story of a police officer facing a harrowing personal crisis.
-
C.
2nd Chance
2nd Chance is the second novel in James Patterson’s Women’s Murder Club crime thriller series, following a team of women professionals who work together to solve a string of brutal murders.
-
D.
Second Chance
Second Chance is a 1953 American film noir crime drama notable for its early use of 3D technology and direction by Rudolph Maté.
-
E.
One Chance
One Chance is a 2013 British biographical comedy-drama film about opera singer Paul Potts, directed by David Frankel.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970f97f9c81908c75259a4cab1d3c |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69bb679f88190a1799b73c3f738b6 |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69fb238f08190a0c63d71bfbe4529 |
completed | May 3, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a087120c81908644ed732eff4d99 |
completed | May 3, 2026, 1:10 a.m. |
Created at: April 9, 2026, 5:38 p.m.