Triple

T3595472
Position Surface form Disambiguated ID Type / Status
Subject Magdalena Echenique Rozas E76128 entity
Predicate givenName P17 FINISHED
Object Magdalena E38830 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: Magdalena | Statement: [Magdalena Echenique Rozas, givenName, Magdalena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magdalena
Context triple: [Magdalena Echenique Rozas, givenName, Magdalena]
  • A. Magdalena chosen
    Magdalena is the given first name of Swedish opera singer and environmental activist Malena Ernman.
  • B. Bassein
    Bassein is a historic coastal town in western India, now known as Vasai, notable for its strategic port and colonial-era fortifications that played a key role in regional power struggles.
  • C. Morava
    Morava is a Central European river that forms part of the border between Austria, the Czech Republic, and Slovakia before joining the Danube near Bratislava.
  • D. Ema
    Ema is a given name used as a variant spelling of Emma in various languages and cultures.
  • E. Freirina
    Freirina is a small town and commune in northern Chile known for its agricultural activity and historic architecture within the Atacama Region.
  • 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_69ad85d8042081908af94a04c410dec0 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc15f41cc819085b3e897d823757d completed March 8, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b403130cf081909bb90800d7dc2d6f completed March 13, 2026, 12:29 p.m.
Created at: March 8, 2026, 3:22 p.m.