Triple
T15176440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Origins |
E362621
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Kashish
Kashish is an actor who appeared in the science fiction drama film "I Origins."
|
E1141100
|
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: Kashish | Statement: [I Origins, castMember, Kashish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kashish Context triple: [I Origins, castMember, Kashish]
-
A.
Nihalani
Nihalani is an Indian film director recognized for his influential contributions to the country's parallel cinema movement.
-
B.
Sairat
Sairat is a critically acclaimed and commercially successful Marathi romantic drama film known for its powerful portrayal of caste and class conflict in rural India.
-
C.
Anjana Vasan
Anjana Vasan is a Singaporean-born Indian actress and singer known for her acclaimed performance in the British comedy series "We Are Lady Parts" and her work on stage and screen in the UK.
-
D.
Zoya Akhtar
Zoya Akhtar is an acclaimed Indian film director and screenwriter known for movies like "Zindagi Na Milegi Dobara," "Dil Dhadakne Do," and the series "Made in Heaven."
-
E.
Annupuri
Annupuri is a prominent mountain and ski area in the Niseko region of Hokkaido, Japan, known for its abundant powder snow and popular winter sports facilities.
- 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: Kashish Triple: [I Origins, castMember, Kashish]
Generated description
Kashish is an actor who appeared in the science fiction drama film "I Origins."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kashish Target entity description: Kashish is an actor who appeared in the science fiction drama film "I Origins."
-
A.
Nihalani
Nihalani is an Indian film director recognized for his influential contributions to the country's parallel cinema movement.
-
B.
Sairat
Sairat is a critically acclaimed and commercially successful Marathi romantic drama film known for its powerful portrayal of caste and class conflict in rural India.
-
C.
Anjana Vasan
Anjana Vasan is a Singaporean-born Indian actress and singer known for her acclaimed performance in the British comedy series "We Are Lady Parts" and her work on stage and screen in the UK.
-
D.
Zoya Akhtar
Zoya Akhtar is an acclaimed Indian film director and screenwriter known for movies like "Zindagi Na Milegi Dobara," "Dil Dhadakne Do," and the series "Made in Heaven."
-
E.
Annupuri
Annupuri is a prominent mountain and ski area in the Niseko region of Hokkaido, Japan, known for its abundant powder snow and popular winter sports facilities.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0066236d481909e8ac47f496861ad |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec89061548190b0b10da00b8d937e |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69fec91a2d708190bcc67793c46b2a61 |
completed | May 9, 2026, 5:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feca0d38088190910dbf4f2538a9d4 |
completed | May 9, 2026, 5:45 a.m. |
Created at: April 10, 2026, 3:09 a.m.