Triple

T7863818
Position Surface form Disambiguated ID Type / Status
Subject Russell 1000 Growth Index E182564 entity
Predicate dataVendorCode P508 FINISHED
Object RLG
RLG is the data vendor code used to identify the Russell 1000 Growth Index, a major U.S. equity benchmark focused on large-cap growth stocks.
 E700930 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: RLG | Statement: [Russell 1000 Growth Index, dataVendorCode, RLG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RLG
Context triple: [Russell 1000 Growth Index, dataVendorCode, RLG]
  • A. RGL
    RGL is the IATA airport code for Piloto Civil Norberto Fernández International Airport serving Río Gallegos in southern Argentina.
  • B. RLD
    RLD is an Indian political party, primarily influential in the state of Uttar Pradesh, known for representing agrarian and rural interests.
  • C. RLM
    RLM was the abbreviation for the Reich Air Ministry, the government department responsible for overseeing aviation and the Luftwaffe in Nazi Germany.
  • D. RLC
    RLC is the Royal Logistic Corps, a branch of the British Army responsible for providing logistics support including supply, transport, and distribution.
  • E. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • 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: RLG
Triple: [Russell 1000 Growth Index, dataVendorCode, RLG]
Generated description
RLG is the data vendor code used to identify the Russell 1000 Growth Index, a major U.S. equity benchmark focused on large-cap growth stocks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RLG
Target entity description: RLG is the data vendor code used to identify the Russell 1000 Growth Index, a major U.S. equity benchmark focused on large-cap growth stocks.
  • A. RGL
    RGL is the IATA airport code for Piloto Civil Norberto Fernández International Airport serving Río Gallegos in southern Argentina.
  • B. RLD
    RLD is an Indian political party, primarily influential in the state of Uttar Pradesh, known for representing agrarian and rural interests.
  • C. RLM
    RLM was the abbreviation for the Reich Air Ministry, the government department responsible for overseeing aviation and the Luftwaffe in Nazi Germany.
  • D. RLC
    RLC is the Royal Logistic Corps, a branch of the British Army responsible for providing logistics support including supply, transport, and distribution.
  • E. RL
    RL is the commonly used acronym for the U.S. Department of Energy’s Richland Operations Office, which oversees environmental cleanup and related activities at the Hanford Site in Washington State.
  • 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_69ca82887fd48190975896bf38c4596b completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb36bfd8a4819093c2ef6d47891a68 completed March 31, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b51c01481909c34a8d0efb89577 completed March 31, 2026, 5:27 a.m.
NEDg Description generation batch_69cb762fda2c81908ed508e12cabb938 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbbf706e888190bfd08d9d78945c49 completed March 31, 2026, 12:34 p.m.
Created at: March 30, 2026, 4:54 p.m.