Triple

T25224324
Position Surface form Disambiguated ID Type / Status
Subject California-class starship E632050 entity
Predicate hasNotableExample P1259 FINISHED
Object USS San Clemente
USS San Clemente is a starship of the California class in the Star Trek universe, typically serving in support and second-contact roles within Starfleet.
E1845310 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: USS San Clemente | Statement: [California-class starship, hasNotableExample, USS San Clemente]
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: USS San Clemente
Triple: [California-class starship, hasNotableExample, USS San Clemente]
Generated description
USS San Clemente is a starship of the California class in the Star Trek universe, typically serving in support and second-contact roles within Starfleet.

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_69e75a8e0f688190a7aebe9a4815e25b completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f47cc23da88190bad7b9a28e09898b completed May 1, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a25a829d6b08190af6c336fdd7f38c8 completed June 7, 2026, 5:19 p.m.
NEDg Description generation batch_6a25aca216088190b6e106c9172f638c completed June 7, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_6a25b146e9d0819086b956ae8ea30aab completed June 7, 2026, 5:58 p.m.
Created at: April 21, 2026, 1:03 p.m.