Triple
T20386849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Run Fatboy Run |
E497981
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
India de Beaufort
India de Beaufort is a British actress and singer known for her roles in film and television, including appearances in comedies and fantasy series.
|
E1428086
|
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: India de Beaufort | Statement: [Run Fatboy Run, starring, India de Beaufort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: India de Beaufort Context triple: [Run Fatboy Run, starring, India de Beaufort]
-
A.
Beynes
Beynes is a commune in the Yvelines department of the Île-de-France region in north-central France.
-
B.
Brewster
Brewster is the given name of Brewster Kahle, an American computer engineer and digital librarian best known as the founder of the Internet Archive.
-
C.
Brewster
Brewster is an English occupational surname historically associated with brewing ale or beer.
-
D.
Brewster
Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
-
E.
Brewster
Brewster is a small hamlet and census-designated place in Putnam County, New York, known for its historic downtown and role as a local commercial and transportation hub.
- 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: India de Beaufort Triple: [Run Fatboy Run, starring, India de Beaufort]
Generated description
India de Beaufort is a British actress and singer known for her roles in film and television, including appearances in comedies and fantasy series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: India de Beaufort Target entity description: India de Beaufort is a British actress and singer known for her roles in film and television, including appearances in comedies and fantasy series.
-
A.
Beynes
Beynes is a commune in the Yvelines department of the Île-de-France region in north-central France.
-
B.
Brewster
Brewster is the given name of Brewster Kahle, an American computer engineer and digital librarian best known as the founder of the Internet Archive.
-
C.
Brewster
Brewster is an English occupational surname historically associated with brewing ale or beer.
-
D.
Brewster
Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
-
E.
Brewster
Brewster is a small hamlet and census-designated place in Putnam County, New York, known for its historic downtown and role as a local commercial and transportation hub.
- 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_69e0b4a71ebc8190b153a36c738730f4 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6790bcef481909453d19c846ab420 |
completed | April 20, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08761d20a48190ad378eba3d22684a |
completed | May 16, 2026, 1:50 p.m. |
| NEDg | Description generation | batch_6a0876f82dac8190a6c25fc0574a842a |
completed | May 16, 2026, 1:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0877ac99408190a31ee21894770334 |
completed | May 16, 2026, 1:57 p.m. |
Created at: April 16, 2026, 11:28 a.m.