Triple

T12642561
Position Surface form Disambiguated ID Type / Status
Subject Sebastian Castellanos E301933 entity
Predicate setting P1957 FINISHED
Object Krimson City E914395 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: Krimson City | Statement: [Sebastian Castellanos, setting, Krimson City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Krimson City
Context triple: [Sebastian Castellanos, setting, Krimson City]
  • A. Krimson City chosen
    Krimson City is a dark, nightmarish urban environment central to the events of the survival horror game series The Evil Within.
  • B. Crown City
    Crown City is a nickname for Pasadena, California, highlighting its reputation as an elegant, historically rich city known for events like the Rose Parade.
  • C. Redshore City
    Redshore City is a glitzy, entertainment-focused metropolis in the animated film "Sing 2," inspired by places like Las Vegas and Hollywood.
  • D. Archangel City
    Archangel City is an alternative name for the Russian port city of Arkhangelsk, a historic hub of Arctic trade and shipbuilding on the White Sea.
  • E. Radiant City
    Radiant City is Le Corbusier’s influential modernist urban planning vision that proposed high-density towers set in open green spaces, strict zoning, and car-oriented infrastructure to create an ordered, efficient city.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d7bdec9f9c8190b4bac675b7588211 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9614ae6ac8190b42acbf2b0331fda completed April 10, 2026, 8:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6687770388190b4777885dae8a38f completed May 2, 2026, 9:11 p.m.
Created at: April 9, 2026, 5:17 p.m.