Triple
T11195034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Yasmin Aga Khan |
E264900
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Yasmin
Yasmin is a feminine given name of Persian and Arabic origin, commonly associated with the jasmine flower and used in many cultures worldwide.
|
E910369
|
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: [Princess Yasmin Aga Khan, givenName, Yasmin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yasmin Context triple: [Princess Yasmin Aga Khan, givenName, 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.
Yasmina
Yasmina is the given first name of French actress Isabelle Adjani, reflecting her Algerian heritage.
-
C.
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.
-
D.
Samira
Samira is a feminine given name of Arabic origin commonly used across the Middle East, North Africa, and South Asia.
-
E.
Laiyah
Laiyah is a musical artist known for collaborating as a featured performer on the track "Code Red."
- 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: [Princess Yasmin Aga Khan, givenName, Yasmin]
Generated description
Yasmin is a feminine given name of Persian and Arabic origin, commonly associated with the jasmine flower and used in many cultures worldwide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yasmin Target entity description: Yasmin is a feminine given name of Persian and Arabic origin, commonly associated with the jasmine flower and used in many cultures worldwide.
-
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.
Yasmina
Yasmina is the given first name of French actress Isabelle Adjani, reflecting her Algerian heritage.
-
C.
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.
-
D.
Samira
Samira is a feminine given name of Arabic origin commonly used across the Middle East, North Africa, and South Asia.
-
E.
Laiyah
Laiyah is a musical artist known for collaborating as a featured performer on the track "Code Red."
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8bf14e481908563b15790af4d20 |
completed | April 9, 2026, 5:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4840640688190a5b3c36883b8fce8 |
completed | April 19, 2026, 7:28 a.m. |
| NEDg | Description generation | batch_69e48717c35481908fb05597084167e7 |
completed | April 19, 2026, 7:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e48875faa88190af33654e6d9a708b |
completed | April 19, 2026, 7:47 a.m. |
Created at: April 8, 2026, 9:29 p.m.