Triple
T11663414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dar & Receber |
E277181
|
entity |
| Predicate | hasTitleTranslation |
P2303
|
FINISHED |
| Object |
Give & Receive
Give & Receive is the English title of the Brazilian film "Dar & Receber."
|
E939093
|
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: Give & Receive | Statement: [Dar & Receber, hasTitleTranslation, Give & Receive]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Give & Receive Context triple: [Dar & Receber, hasTitleTranslation, Give & Receive]
-
A.
Giving
Giving is the sequel to Bill Clinton’s memoir "My Life," focusing on philanthropy and the power of citizen service to change the world.
-
B.
Giving 2.0
Giving 2.0 is a book by Laura Arrillaga-Andreessen that offers a modern, strategic approach to effective philanthropy for individual donors.
-
C.
Please Give
Please Give is a 2010 indie dramedy film written and directed by Nicole Holofcener that explores guilt, privilege, and urban relationships among affluent New Yorkers.
-
D.
Cadeau
Cadeau is a famous 1921 Dada/Surrealist readymade sculpture by Man Ray consisting of a flat iron with a row of metal tacks affixed to its soleplate.
-
E.
Gift
Gift is a novel by Norwegian author Alexander Kielland, known for its critical portrayal of bourgeois society and arranged marriage in 19th-century Norway.
- 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: Give & Receive Triple: [Dar & Receber, hasTitleTranslation, Give & Receive]
Generated description
Give & Receive is the English title of the Brazilian film "Dar & Receber."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Give & Receive Target entity description: Give & Receive is the English title of the Brazilian film "Dar & Receber."
-
A.
Giving
Giving is the sequel to Bill Clinton’s memoir "My Life," focusing on philanthropy and the power of citizen service to change the world.
-
B.
Giving 2.0
Giving 2.0 is a book by Laura Arrillaga-Andreessen that offers a modern, strategic approach to effective philanthropy for individual donors.
-
C.
Please Give
Please Give is a 2010 indie dramedy film written and directed by Nicole Holofcener that explores guilt, privilege, and urban relationships among affluent New Yorkers.
-
D.
Cadeau
Cadeau is a famous 1921 Dada/Surrealist readymade sculpture by Man Ray consisting of a flat iron with a row of metal tacks affixed to its soleplate.
-
E.
Gift
Gift is a novel by Norwegian author Alexander Kielland, known for its critical portrayal of bourgeois society and arranged marriage in 19th-century Norway.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a3d3a64c819099f398ea22c8c180 |
completed | April 10, 2026, 7:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee882dbf2481909ffcae5f80cd494f |
completed | April 26, 2026, 9:48 p.m. |
| NEDg | Description generation | batch_69eeb316ba948190a39dfc80d6245b17 |
completed | April 27, 2026, 12:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69eee9b59f448190b97ecd05c843ce78 |
completed | April 27, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:39 p.m.