Triple

T17826362
Position Surface form Disambiguated ID Type / Status
Subject Paris-Austerlitz station E445126 entity
Predicate IATACode P418 FINISHED
Object XGB
XGB is the IATA station code assigned to Paris-Austerlitz railway station in Paris, France.
 E1289432 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: XGB | Statement: [Paris-Austerlitz station, IATACode, XGB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XGB
Context triple: [Paris-Austerlitz station, IATACode, XGB]
  • A. XGBoost
    XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
  • B. CatBoost
    CatBoost is an open-source gradient boosting library developed by Yandex, optimized for handling categorical features and delivering high-performance machine learning models.
  • C. LightGBM
    LightGBM is a high-performance, gradient boosting framework based on decision trees, designed for speed and efficiency in large-scale machine learning tasks.
  • D. GXF
    GXF is an open source LF Energy project that provides a scalable, secure platform for managing and integrating smart grid and energy devices.
  • E. LXGB
    LXGB is the ICAO airport code for Gibraltar International Airport, a unique airfield known for its runway intersecting a major road near the Rock of Gibraltar.
  • 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: XGB
Triple: [Paris-Austerlitz station, IATACode, XGB]
Generated description
XGB is the IATA station code assigned to Paris-Austerlitz railway station in Paris, France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: XGB
Target entity description: XGB is the IATA station code assigned to Paris-Austerlitz railway station in Paris, France.
  • A. XGBoost
    XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
  • B. CatBoost
    CatBoost is an open-source gradient boosting library developed by Yandex, optimized for handling categorical features and delivering high-performance machine learning models.
  • C. LightGBM
    LightGBM is a high-performance, gradient boosting framework based on decision trees, designed for speed and efficiency in large-scale machine learning tasks.
  • D. GXF
    GXF is an open source LF Energy project that provides a scalable, secure platform for managing and integrating smart grid and energy devices.
  • E. LXGB
    LXGB is the ICAO airport code for Gibraltar International Airport, a unique airfield known for its runway intersecting a major road near the Rock of Gibraltar.
  • 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_69d8b9f0de78819099395b14db75a8a6 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48914e20481908883d1da194f446c completed April 19, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02ff6d07308190bcf6c959204f89a6 completed May 12, 2026, 10:22 a.m.
NEDg Description generation batch_6a03003084fc8190b7272f7d2e0735d7 completed May 12, 2026, 10:25 a.m.
NED2 Entity disambiguation (via description) batch_6a0300e885e481909c76dfbac2fd1009 completed May 12, 2026, 10:28 a.m.
Created at: April 10, 2026, 10:15 a.m.