Triple

T20809356
Position Surface form Disambiguated ID Type / Status
Subject Denison Mines E512254 entity
Predicate tickerSymbol P1447 FINISHED
Object DNN
DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
 E1451685 NE FINISHED

How this triple was built (4 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: DNN | Statement: [Denison Mines, tickerSymbol, DNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DNN
Context triple: [Denison Mines, tickerSymbol, DNN]
  • A. FNN
    FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
  • B. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • C. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • D. nnd
    nnd is the ISO 639-3 code for the Nendö language, an Oceanic language spoken in the Solomon Islands.
  • E. BNNS
    BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: DNN
Triple: [Denison Mines, tickerSymbol, DNN]
Generated description
DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DNN
Target entity description: DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • A. FNN
    FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
  • B. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • C. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • D. nnd
    nnd is the ISO 639-3 code for the Nendö language, an Oceanic language spoken in the Solomon Islands.
  • E. BNNS
    BNNS (Basic Neural Network Subroutines) is Apple’s low-level, hardware-accelerated framework for performing neural network and machine learning computations efficiently on Apple devices.
  • F. None of above. chosen

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_69e0b4cd25088190b48ca9700cd24efc completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2d199888190b8b190c928f510b7 completed April 21, 2026, 12:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08f8c979d481909dfa2aa97b1cefab completed May 16, 2026, 11:07 p.m.
NEDg Description generation batch_6a08f9deb9248190af178b5c28728e00 completed May 16, 2026, 11:12 p.m.
NED2 Entity disambiguation (via description) batch_6a08fa80202c8190929f8f1b47d6fae4 completed May 16, 2026, 11:15 p.m.
Created at: April 16, 2026, 12:40 p.m.