Triple

T37418941
Position Surface form Disambiguated ID Type / Status
Subject J. Richard Gott E929792 entity
Predicate knownFor P22 FINISHED
Object Gott cosmic string model
The Gott cosmic string model is a theoretical construct in general relativity proposing that rapidly moving cosmic strings could create closed timelike curves, allowing for the possibility of time travel.
E2227581 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: Gott cosmic string model | Statement: [J. Richard Gott, knownFor, Gott cosmic string model]
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: Gott cosmic string model
Triple: [J. Richard Gott, knownFor, Gott cosmic string model]
Generated description
The Gott cosmic string model is a theoretical construct in general relativity proposing that rapidly moving cosmic strings could create closed timelike curves, allowing for the possibility of time travel.

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_69f76ebde49481908566cd96b37ccc84 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb8d8b54148190b04c94451cc631c3 completed May 6, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40824ad67081909308c3bcc6fc8ee5 completed June 28, 2026, 2:09 a.m.
NEDg Description generation batch_6a4082c3d44c8190bcf3090e1fbcb069 completed June 28, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_6a40839c76388190b3ac6bda25481e03 completed June 28, 2026, 2:14 a.m.
Created at: May 3, 2026, 4:16 p.m.