Triple

T38283195
Position Surface form Disambiguated ID Type / Status
Subject Team Zissou E1022131 entity
Predicate hasMember P10 FINISHED
Object Vikram Ray
Vikram Ray is a member of the fictional oceanographic crew Team Zissou from Wes Anderson’s film "The Life Aquatic with Steve Zissou."
E2286276 NE FINISHED

How this triple was built (2 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: Vikram Ray | Statement: [Team Zissou, hasMember, Vikram Ray]
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: Vikram Ray
Triple: [Team Zissou, hasMember, Vikram Ray]
Generated description
Vikram Ray is a member of the fictional oceanographic crew Team Zissou from Wes Anderson’s film "The Life Aquatic with Steve Zissou."

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_69f76df0cddc81908d16c1556ff4097f completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69fcc5d39d688190a1025d6295529bb7 completed May 7, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_6a465ea4b0b081908bbc72cde3eed0ec completed July 2, 2026, 12:50 p.m.
NEDg Description generation batch_6a465f37d78081909f4cdede5eae3ef6 completed July 2, 2026, 12:53 p.m.
NED2 Entity disambiguation (via description) batch_6a469d2b95548190aabde4ef0e5f84ed completed July 2, 2026, 5:17 p.m.
Created at: May 3, 2026, 4:30 p.m.