Triple

T21614416
Position Surface form Disambiguated ID Type / Status
Subject Krieger E533395 entity
Predicate hasNotableBearer P458 FINISHED
Object Martin H. Krieger
Martin H. Krieger is an American scholar known for his work in urban planning, public policy, and the philosophy of science, particularly through his long tenure as a professor at the University of Southern California.
 E1755910 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: Martin H. Krieger | Statement: [Krieger, hasNotableBearer, Martin H. Krieger]
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: Martin H. Krieger
Triple: [Krieger, hasNotableBearer, Martin H. Krieger]
Generated description
Martin H. Krieger is an American scholar known for his work in urban planning, public policy, and the philosophy of science, particularly through his long tenure as a professor at the University of Southern California.

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_69e0c46411108190bba0d4176dffc9f3 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef3ba9aca48190b5180eefd61a9fdc completed April 27, 2026, 10:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1247c6f6cc8190ad5c32aa57f7b78d completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a12489d7498819083fb008e2acff886 completed May 24, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a124918ab688190b6172f571d3aba73 completed May 24, 2026, 12:40 a.m.
Created at: April 16, 2026, 6:33 p.m.