Triple

T8517379
Position Surface form Disambiguated ID Type / Status
Subject Rainer Weiss E201607 entity
Predicate givenName P17 FINISHED
Object Rainer E590306 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: Rainer | Statement: [Rainer Weiss, givenName, Rainer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rainer
Context triple: [Rainer Weiss, givenName, Rainer]
  • A. Rainer chosen
    Rainer is a masculine given name of Germanic origin, related to names like Ragnar and Rayner, historically borne by various European nobles, clerics, and modern public figures.
  • B. Othmar
    Othmar is a masculine given name of Germanic origin, notably borne by the Swiss-American civil engineer Othmar Ammann.
  • C. Stahlecker
    Stahlecker is a German-language surname most notably associated with Franz Walter Stahlecker, a high-ranking SS officer and Nazi official during World War II.
  • D. Gustav
    Gustav is a masculine given name of German origin, borne by several notable historical figures including scientists, artists, and royalty.
  • E. Urich
    Urich is a surname most notably associated with American actor Robert Urich, known for his roles in television series such as "Spenser: For Hire" and "Vega$."
  • 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe62550908190af882019d68a904a completed March 31, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e65419481909e787066fd069565 completed April 2, 2026, 11:09 a.m.
Created at: March 30, 2026, 6:15 p.m.