Triple

T15820808
Position Surface form Disambiguated ID Type / Status
Subject Harrisburg University of Science and Technology E383600 entity
Predicate abbreviation P43 FINISHED
Object HU
HU is a private STEM-focused university located in Harrisburg, Pennsylvania, known for its programs in science, technology, engineering, and mathematics.
E1178530 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: HU | Statement: [Harrisburg University of Science and Technology, abbreviation, HU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HU
Context triple: [Harrisburg University of Science and Technology, abbreviation, HU]
  • A. HU
    HU is the postcode area covering Kingston upon Hull and its surrounding region in the United Kingdom.
  • B. HU
    HU is the vehicle registration code used on license plates for the German town of Hanau.
  • C. HU
    HU is the IATA airline designator assigned to Hainan Airlines, a major Chinese carrier.
  • D. HU
    HU is the ISO 3166-1 alpha-2 country code for Hungary, a landlocked Central European nation known for its capital Budapest and rich cultural history.
  • E. HU
    HU is the commonly used abbreviation for Hogeschool Utrecht, a large university of applied sciences in the Netherlands.
  • 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: HU
Triple: [Harrisburg University of Science and Technology, abbreviation, HU]
Generated description
HU is a private STEM-focused university located in Harrisburg, Pennsylvania, known for its programs in science, technology, engineering, and mathematics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: HU
Target entity description: HU is a private STEM-focused university located in Harrisburg, Pennsylvania, known for its programs in science, technology, engineering, and mathematics.
  • A. HU
    HU is the postcode area covering Kingston upon Hull and its surrounding region in the United Kingdom.
  • B. HU
    HU is the IATA airline designator assigned to Hainan Airlines, a major Chinese carrier.
  • C. HU
    HU is the ISO 3166-1 alpha-2 country code for Hungary, a landlocked Central European nation known for its capital Budapest and rich cultural history.
  • D. HU
    HU is the commonly used abbreviation for Hogeschool Utrecht, a large university of applied sciences in the Netherlands.
  • E. HU
    HU is the vehicle registration code used on license plates for the German town of Hanau.
  • 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_69e0c4a7ba4881908a2747bf063d79f1 completed April 16, 2026, 11:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff99999c2c8190b1838aed40e12061 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.