Triple

T4447290
Position Surface form Disambiguated ID Type / Status
Subject Misrata International Airport E96320 entity
Predicate hasICAOcode P419 FINISHED
Object HLMS
HLMS is the ICAO airport code assigned to Misrata International Airport in Misrata, Libya.
E440979 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: HLMS | Statement: [Misrata International Airport, hasICAOcode, HLMS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HLMS
Context triple: [Misrata International Airport, hasICAOcode, HLMS]
  • A. HAL
    HAL is the ICAO airline designator used to identify Hawaiian Airlines in international aviation operations.
  • B. HAL
    HAL is an open-access multidisciplinary archive and repository for scholarly documents, widely used by researchers to share and preserve their scientific publications.
  • C. HAL
    HAL is the vehicle registration code used on license plates for the German city of Halle (Saale).
  • D. HAL
    HAL is the stock ticker symbol for Halliburton Company, a major American oilfield services and energy industry equipment provider.
  • E. HL
    HL is the vehicle registration code used on license plates for the German city of Lübeck.
  • 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: HLMS
Triple: [Misrata International Airport, hasICAOcode, HLMS]
Generated description
HLMS is the ICAO airport code assigned to Misrata International Airport in Misrata, Libya.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: HLMS
Target entity description: HLMS is the ICAO airport code assigned to Misrata International Airport in Misrata, Libya.
  • A. HAL
    HAL is the ICAO airline designator used to identify Hawaiian Airlines in international aviation operations.
  • B. HAL
    HAL is an open-access multidisciplinary archive and repository for scholarly documents, widely used by researchers to share and preserve their scientific publications.
  • C. HAL
    HAL is the vehicle registration code used on license plates for the German city of Halle (Saale).
  • D. HAL
    HAL is the stock ticker symbol for Halliburton Company, a major American oilfield services and energy industry equipment provider.
  • E. HL
    HL is the vehicle registration code used on license plates for the German city of Lübeck.
  • 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_69b345415ba481908df738e7174448ba completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355d31e10819086590b9f828d50b0 completed March 13, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b61386df48819080e44a23b9d67d23 completed March 15, 2026, 2:03 a.m.
NEDg Description generation batch_69b617c13d4481909d22d201ce405d3a completed March 15, 2026, 2:21 a.m.
NED2 Entity disambiguation (via description) batch_69b6187687f8819084e2d611e9e31f79 completed March 15, 2026, 2:24 a.m.
Created at: March 12, 2026, 11:32 p.m.