Triple

T24535685
Position Surface form Disambiguated ID Type / Status
Subject NHL legal department E606942 entity
Predicate worksWith P398 FINISHED
Object NHL player safety department
The NHL player safety department is the league office responsible for reviewing on-ice incidents and issuing discipline such as fines and suspensions to ensure player safety and enforce the rules.
 E1639242 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: NHL player safety department | Statement: [NHL legal department, worksWith, NHL player safety department]
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: NHL player safety department
Triple: [NHL legal department, worksWith, NHL player safety department]
Generated description
The NHL player safety department is the league office responsible for reviewing on-ice incidents and issuing discipline such as fines and suspensions to ensure player safety and enforce the rules.

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_69e2c4c90c848190b23c4303620dcaaf completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a89ece0c8190b0c9bc13a0b15174 completed April 30, 2026, 12:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0feea183d081908450a93ed32a6009 completed May 22, 2026, 5:50 a.m.
NEDg Description generation batch_6a0ff0c57b088190b031ea186a987e32 completed May 22, 2026, 5:59 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff16636008190a4267f6b8d8e3bb2 completed May 22, 2026, 6:02 a.m.
Created at: April 18, 2026, 2:26 a.m.