Triple
T5140621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busby Berkeley |
E115940
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Dames
Dames is a 1934 Warner Bros. musical comedy film famed for its elaborate, visually inventive song-and-dance sequences choreographed by Busby Berkeley.
|
E497900
|
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: Dames | Statement: [Busby Berkeley, notableWork, Dames]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dames Context triple: [Busby Berkeley, notableWork, Dames]
-
A.
Dame
Dame is a British honorific title bestowed primarily upon women in recognition of significant contributions to national life, often in the arts, public service, or other distinguished fields.
-
B.
Dame
Dame is the popular nickname of NBA All-Star point guard Damian Lillard, known for his clutch shooting and leadership.
-
C.
Madame
Madame was the popular nickname of Henrietta of England, Duchess of Orléans, a 17th-century English princess who became a prominent figure at the French court of Louis XIV.
-
D.
Ladies
The Ladies are the women's athletic teams representing Centenary College of Louisiana in intercollegiate sports.
-
E.
Madam
"Madam" is a formal term of address for a woman, often used to show respect or politeness in social, professional, or official contexts.
- 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: Dames Triple: [Busby Berkeley, notableWork, Dames]
Generated description
Dames is a 1934 Warner Bros. musical comedy film famed for its elaborate, visually inventive song-and-dance sequences choreographed by Busby Berkeley.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dames Target entity description: Dames is a 1934 Warner Bros. musical comedy film famed for its elaborate, visually inventive song-and-dance sequences choreographed by Busby Berkeley.
-
A.
Dame
Dame is a British honorific title bestowed primarily upon women in recognition of significant contributions to national life, often in the arts, public service, or other distinguished fields.
-
B.
Dame
Dame is the popular nickname of NBA All-Star point guard Damian Lillard, known for his clutch shooting and leadership.
-
C.
Madame
Madame was the popular nickname of Henrietta of England, Duchess of Orléans, a 17th-century English princess who became a prominent figure at the French court of Louis XIV.
-
D.
Ladies
The Ladies are the women's athletic teams representing Centenary College of Louisiana in intercollegiate sports.
-
E.
Madam
"Madam" is a formal term of address for a woman, often used to show respect or politeness in social, professional, or official contexts.
- 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_69bd44459a988190a772a5c2ec6a1965 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd787e5fe88190834042a73d4d9619 |
completed | March 20, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfe2a59881908c790e26a2365353 |
completed | March 21, 2026, 5:05 p.m. |
| NEDg | Description generation | batch_69bed07c9cd081908f8246e24b2f6458 |
completed | March 21, 2026, 5:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bed1013fe88190b76855a042226359 |
completed | March 21, 2026, 5:10 p.m. |
Created at: March 20, 2026, 1:43 p.m.