Triple

T25532033
Position Surface form Disambiguated ID Type / Status
Subject Robinson Crusoe on Mars E639944 entity
Predicate starring P1507 FINISHED
Object Victor Lundin
Victor Lundin was an American character actor and singer best known to sci-fi fans for his role in the 1964 film "Robinson Crusoe on Mars" and for numerous appearances in television series of the 1960s and 1970s.
E1706618 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: Victor Lundin | Statement: [Robinson Crusoe on Mars, starring, Victor Lundin]
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: Victor Lundin
Triple: [Robinson Crusoe on Mars, starring, Victor Lundin]
Generated description
Victor Lundin was an American character actor and singer best known to sci-fi fans for his role in the 1964 film "Robinson Crusoe on Mars" and for numerous appearances in television series of the 1960s and 1970s.

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_69e75dbf3f9c8190b3f2a75d1b75d127 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f863b97481908c64be433f36980e completed May 2, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111ae7e6008190939e7d418b390414 completed May 23, 2026, 3:11 a.m.
NEDg Description generation batch_6a111c7f25788190ab64d5bd35a691c3 completed May 23, 2026, 3:18 a.m.
NED2 Entity disambiguation (via description) batch_6a111d2b601881909f79329b949194e5 completed May 23, 2026, 3:21 a.m.
Created at: April 21, 2026, 3:15 p.m.