Triple

T34986030
Position Surface form Disambiguated ID Type / Status
Subject Type-11 shuttlecraft E1008948 entity
Predicate predecessor P97 FINISHED
Object Type-9 shuttlecraft
The Type-9 shuttlecraft is a compact, high-speed Starfleet auxiliary vessel featured in Star Trek, commonly used for short-range missions and personnel transport in the late 24th century.
E2123409 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: Type-9 shuttlecraft | Statement: [Type-11 shuttlecraft, predecessor, Type-9 shuttlecraft]
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: Type-9 shuttlecraft
Triple: [Type-11 shuttlecraft, predecessor, Type-9 shuttlecraft]
Generated description
The Type-9 shuttlecraft is a compact, high-speed Starfleet auxiliary vessel featured in Star Trek, commonly used for short-range missions and personnel transport in the late 24th century.

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_69f76dc844a48190881951fffb83d17e completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7849d996481909baf92a2d8b69133 completed May 3, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37c623f9cc81908213ceb2e8be7ef2 completed June 21, 2026, 11:08 a.m.
NEDg Description generation batch_6a37c6a375b08190a4fed21a96ca40d3 completed June 21, 2026, 11:10 a.m.
NED2 Entity disambiguation (via description) batch_6a37c73c47b08190a691c8afb098a9f3 completed June 21, 2026, 11:13 a.m.
Created at: May 3, 2026, 4:01 p.m.