Triple

T25943937
Position Surface form Disambiguated ID Type / Status
Subject SS President Coolidge E653785 entity
Predicate operator P179 FINISHED
Object American President Lines
American President Lines was a major U.S. shipping company known for operating a fleet of passenger and cargo vessels on trans-Pacific and other international routes.
 E1700717 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: American President Lines | Statement: [SS President Coolidge, operator, American President Lines]
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: American President Lines
Triple: [SS President Coolidge, operator, American President Lines]
Generated description
American President Lines was a major U.S. shipping company known for operating a fleet of passenger and cargo vessels on trans-Pacific and other international routes.

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_69e7ab3fd2f881908837305e4ba98011 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f6046115b88190aa9011f53a54cddc completed May 2, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ece2bd148190aa01eb324ae4e487 completed May 22, 2026, 11:55 p.m.
NEDg Description generation batch_6a10ee62df94819093fe3a38e8305ee0 completed May 23, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_6a10ef523db88190804391458feb600d completed May 23, 2026, 12:05 a.m.
Created at: April 22, 2026, 8:41 a.m.