Triple

T15810627
Position Surface form Disambiguated ID Type / Status
Subject Houston Airport System E383339 entity
Predicate alsoKnownAs P39 FINISHED
Object HAS
HAS is the abbreviation for the Houston Airport System, the authority that manages and operates Houston’s major public airports.
 E1178460 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: HAS | Statement: [Houston Airport System, alsoKnownAs, HAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HAS
Context triple: [Houston Airport System, alsoKnownAs, HAS]
  • A. HAS
    HAS is the stock ticker symbol for Hasbro, Inc., a major American toy and entertainment company traded on the NASDAQ.
  • B. HAS
    HAS is the IATA airport code for Ha'il Regional Airport in Ha'il, Saudi Arabia.
  • C. Ha
    "Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
  • D. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • E. HASP
    HASP (Houston Automatic Spooling Priority) was an early IBM mainframe job entry and spooling system that managed batch workloads and printer output, serving as a foundation for later systems like JES2.
  • 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: HAS
Triple: [Houston Airport System, alsoKnownAs, HAS]
Generated description
HAS is the abbreviation for the Houston Airport System, the authority that manages and operates Houston’s major public airports.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HAS
Target entity description: HAS is the abbreviation for the Houston Airport System, the authority that manages and operates Houston’s major public airports.
  • A. HAS
    HAS is the stock ticker symbol for Hasbro, Inc., a major American toy and entertainment company traded on the NASDAQ.
  • B. HAS
    HAS is the IATA airport code for Ha'il Regional Airport in Ha'il, Saudi Arabia.
  • C. Ha
    "Ha" is a track by rapper Juvenile, notable for its distinctive second-person narrative style and repetitive use of the word "ha," from his influential 1998 album *400 Degreez*.
  • D. HAV
    HAV is the IATA airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • E. HASP
    HASP (Houston Automatic Spooling Priority) was an early IBM mainframe job entry and spooling system that managed batch workloads and printer output, serving as a foundation for later systems like JES2.
  • 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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0b52aae14819091de08630e7e1d1a completed April 16, 2026, 10:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff999210148190baa6dcb19be3a1d3 completed May 9, 2026, 8:31 p.m.
NEDg Description generation batch_69ff9aa845348190907116612d2c87cd completed May 9, 2026, 8:35 p.m.
NED2 Entity disambiguation (via description) batch_69ff9b8833b88190967db29027b5f987 completed May 9, 2026, 8:39 p.m.
Created at: April 10, 2026, 4:49 a.m.