Triple

T23664622
Position Surface form Disambiguated ID Type / Status
Subject Laurent E584541 entity
Predicate visits P60586 FINISHED
Object the Denali coven in Alaska
The Denali coven in Alaska is a vegetarian vampire coven allied with the Cullens in the Twilight series, known for its beautiful, cultured members and its strict abstinence from human blood.
E1595954 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: the Denali coven in Alaska | Statement: [Laurent, visits, the Denali coven in Alaska]
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: the Denali coven in Alaska
Triple: [Laurent, visits, the Denali coven in Alaska]
Generated description
The Denali coven in Alaska is a vegetarian vampire coven allied with the Cullens in the Twilight series, known for its beautiful, cultured members and its strict abstinence from human blood.

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_69e24901421881908c17a5293bdd4a8e completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b40ad45c8190a3a0ae7c7f9bf3a5 completed April 29, 2026, 7:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f45b67ed481909e7537c4cdd31724 completed May 21, 2026, 5:49 p.m.
NEDg Description generation batch_6a0f47336054819084117d5f59c7b7df completed May 21, 2026, 5:56 p.m.
NED2 Entity disambiguation (via description) batch_6a0f48771d848190950327a6923eb080 completed May 21, 2026, 6:01 p.m.
Created at: April 17, 2026, 6:50 p.m.