Triple

T14563329
Position Surface form Disambiguated ID Type / Status
Subject Hale railway station E341720 entity
Predicate stationCode P1289 FINISHED
Object HAL
HAL is the National Rail station code for Hale railway station in Greater Manchester, England.
E1107201 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: HAL | Statement: [Hale railway station, stationCode, HAL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HAL
Context triple: [Hale railway station, stationCode, HAL]
  • A. HAL
    HAL is the stock ticker symbol for Halliburton Company, a major American oilfield services and energy industry equipment provider.
  • B. HAL
    HAL is the ICAO airline designator used to identify Hawaiian Airlines in international aviation operations.
  • C. HAL
    HAL is the vehicle registration code used on license plates for the German city of Halle (Saale).
  • D. HAL
    HAL is an open-access multidisciplinary archive and repository for scholarly documents, widely used by researchers to share and preserve their scientific publications.
  • E. HAL
    HAL is a major Indian state-owned aerospace and defense company that designs, manufactures, and maintains aircraft, helicopters, and related systems.
  • 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: HAL
Triple: [Hale railway station, stationCode, HAL]
Generated description
HAL is the National Rail station code for Hale railway station in Greater Manchester, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: HAL
Target entity description: HAL is the National Rail station code for Hale railway station in Greater Manchester, England.
  • A. HAL
    HAL is the ICAO airline designator used to identify Hawaiian Airlines in international aviation operations.
  • B. HAL
    HAL is the stock ticker symbol for Halliburton Company, a major American oilfield services and energy industry equipment provider.
  • C. HAL
    HAL is the vehicle registration code used on license plates for the German city of Halle (Saale).
  • D. HAL
    HAL is an open-access multidisciplinary archive and repository for scholarly documents, widely used by researchers to share and preserve their scientific publications.
  • E. HAL
    HAL is a major Indian state-owned aerospace and defense company that designs, manufactures, and maintains aircraft, helicopters, and related systems.
  • 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_69d822dcc6248190bed689984bceb0e2 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb38afa8881909c9151b7620949ae completed April 14, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8ac485688190a2917251a7c8721a completed May 8, 2026, 7:03 a.m.
NEDg Description generation batch_69fd8d0701208190917a0a4f5e493586 completed May 8, 2026, 7:13 a.m.
NED2 Entity disambiguation (via description) batch_69fd8da450108190b1ed174db5f100c4 completed May 8, 2026, 7:15 a.m.
Created at: April 10, 2026, 1:23 a.m.