Triple
T15045664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gorgon City |
E379217
|
entity |
| Predicate | collaboratedWith |
P435
|
FINISHED |
| Object |
Yasmin
Yasmin is a British singer and DJ known for her work in electronic and dance music, including collaborations with acts like Gorgon City.
|
E1133855
|
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: Yasmin | Statement: [Gorgon City, collaboratedWith, Yasmin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yasmin Context triple: [Gorgon City, collaboratedWith, Yasmin]
-
A.
Yasmin
Yasmin is the leading female character in the 1926 silent romantic adventure film "The Son of the Sheik," famously starring opposite Rudolph Valentino.
-
B.
Yasmin
Yasmin is a feminine given name of Persian and Arabic origin, commonly associated with the jasmine flower and used in many cultures worldwide.
-
C.
Yasmina
Yasmina is the given first name of French actress Isabelle Adjani, reflecting her Algerian heritage.
-
D.
Yasmin Kafai
Yasmin Kafai is an educational researcher known for her influential work in constructionist learning, particularly around digital media, game design, and creative computing for children.
-
E.
Samira
Samira is a feminine given name of Arabic origin commonly used across the Middle East, North Africa, and South Asia.
- 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: Yasmin Triple: [Gorgon City, collaboratedWith, Yasmin]
Generated description
Yasmin is a British singer and DJ known for her work in electronic and dance music, including collaborations with acts like Gorgon City.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yasmin Target entity description: Yasmin is a British singer and DJ known for her work in electronic and dance music, including collaborations with acts like Gorgon City.
-
A.
Yasmin
Yasmin is the leading female character in the 1926 silent romantic adventure film "The Son of the Sheik," famously starring opposite Rudolph Valentino.
-
B.
Yasmin
Yasmin is a feminine given name of Persian and Arabic origin, commonly associated with the jasmine flower and used in many cultures worldwide.
-
C.
Yasmina
Yasmina is the given first name of French actress Isabelle Adjani, reflecting her Algerian heritage.
-
D.
Yasmin Kafai
Yasmin Kafai is an educational researcher known for her influential work in constructionist learning, particularly around digital media, game design, and creative computing for children.
-
E.
Samira
Samira is a feminine given name of Arabic origin commonly used across the Middle East, North Africa, and South Asia.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded830c3c08190a87b81abbbb75377 |
completed | April 15, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9de54380819084568664b63322d2 |
completed | May 9, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fea0791f1c81908dcad401fa3ac245 |
completed | May 9, 2026, 2:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fea11a35a88190a5ad6f261fd2d9dc |
completed | May 9, 2026, 2:51 a.m. |
Created at: April 10, 2026, 3 a.m.