Triple
T10467343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luck |
E246830
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Sam Greenfield
Sam Greenfield is the perpetually unlucky young woman who becomes the central heroine of the animated fantasy film "Luck," navigating a secret world of good and bad fortune.
|
E865356
|
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: Sam Greenfield | Statement: [Luck, mainCharacter, Sam Greenfield]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Greenfield Context triple: [Luck, mainCharacter, Sam Greenfield]
-
A.
John Greenfield
John Greenfield was an individual significant enough in local or regional history that the city of Greenfield, California, was named in his honor.
-
B.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
C.
Martin Green
Martin Green is a renowned Australian engineer and solar energy researcher recognized as a leading pioneer in photovoltaic technology.
-
D.
Edward Green
Edward Green was the brother of British idealist philosopher T. H. Green, a member of the same prominent 19th-century English family.
-
E.
Christopher Greenbury
Christopher Greenbury was a British film editor best known for his Academy Award–winning work on the 1999 drama "American Beauty."
- 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: Sam Greenfield Triple: [Luck, mainCharacter, Sam Greenfield]
Generated description
Sam Greenfield is the perpetually unlucky young woman who becomes the central heroine of the animated fantasy film "Luck," navigating a secret world of good and bad fortune.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sam Greenfield Target entity description: Sam Greenfield is the perpetually unlucky young woman who becomes the central heroine of the animated fantasy film "Luck," navigating a secret world of good and bad fortune.
-
A.
John Greenfield
John Greenfield was an individual significant enough in local or regional history that the city of Greenfield, California, was named in his honor.
-
B.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
C.
Martin Green
Martin Green is a renowned Australian engineer and solar energy researcher recognized as a leading pioneer in photovoltaic technology.
-
D.
Edward Green
Edward Green was the brother of British idealist philosopher T. H. Green, a member of the same prominent 19th-century English family.
-
E.
Christopher Greenbury
Christopher Greenbury was a British film editor best known for his Academy Award–winning work on the 1999 drama "American Beauty."
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092e3230819098ab444f73c9bd40 |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89ff1cd948190a1ef331fb810bf26 |
completed | April 10, 2026, 7 a.m. |
| NEDg | Description generation | batch_69d8a2b0d8c88190a1a64bd2bbacabbe |
completed | April 10, 2026, 7:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a6560ddc81909d540f78a9413b3e |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 6, 2026, 12:20 p.m.