Triple

T34060975
Position Surface form Disambiguated ID Type / Status
Subject Federal Minister for Foreign Affairs (Germany) E873491 entity
Predicate officeHeldBy P537 FINISHED
Object Annalena Baerbock
Annalena Baerbock is a German politician from the Alliance 90/The Greens who has served as Germany’s Foreign Minister and was her party’s first candidate for chancellor in a federal election.
E2079897 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: Annalena Baerbock | Statement: [Federal Minister for Foreign Affairs (Germany), officeHeldBy, Annalena Baerbock]
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: Annalena Baerbock
Triple: [Federal Minister for Foreign Affairs (Germany), officeHeldBy, Annalena Baerbock]
Generated description
Annalena Baerbock is a German politician from the Alliance 90/The Greens who has served as Germany’s Foreign Minister and was her party’s first candidate for chancellor in a federal election.

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_69f349a4af208190afa14888f9c9fb9d completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f70b9be10c819081f456a7b4f35d15 completed May 3, 2026, 8:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36a046766c8190b6509281dcb2c85b completed June 20, 2026, 2:14 p.m.
NEDg Description generation batch_6a36a21182608190a308ca7a32aaa966 completed June 20, 2026, 2:22 p.m.
NED2 Entity disambiguation (via description) batch_6a36a26cf4b481909a71246d738b0e51 completed June 20, 2026, 2:23 p.m.
Created at: May 1, 2026, 1:52 a.m.