Triple

T17776326
Position Surface form Disambiguated ID Type / Status
Subject Hackensack University Medical Center E443777 entity
Predicate hasAlternativeName P39 FINISHED
Object HUMC
HUMC is a major teaching and research hospital in Hackensack, New Jersey, known for providing comprehensive tertiary and quaternary medical care.
 E1286610 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: HUMC | Statement: [Hackensack University Medical Center, hasAlternativeName, HUMC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HUMC
Context triple: [Hackensack University Medical Center, hasAlternativeName, HUMC]
  • A. HMU
    HMU is the commonly used abbreviation for the Hellenic Mediterranean University, a higher education institution in Greece.
  • B. UNMH
    UNMH is the primary teaching hospital of the University of New Mexico, serving as a major academic medical and trauma center for the state.
  • C. UHMD
    UHMD is the ICAO airport code for Provideniya Bay Airport in Russia’s Chukotka region.
  • D. BUMC
    BUMC is the Boston University Medical Campus, a major academic health center in Boston that integrates medical education, research, and clinical care.
  • E. UNMHA
    UNMHA is a United Nations peace mission established to monitor and support the ceasefire and redeployment of forces in Yemen’s Hudaydah region under the Stockholm Agreement.
  • 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: HUMC
Triple: [Hackensack University Medical Center, hasAlternativeName, HUMC]
Generated description
HUMC is a major teaching and research hospital in Hackensack, New Jersey, known for providing comprehensive tertiary and quaternary medical care.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HUMC
Target entity description: HUMC is a major teaching and research hospital in Hackensack, New Jersey, known for providing comprehensive tertiary and quaternary medical care.
  • A. HMU
    HMU is the commonly used abbreviation for the Hellenic Mediterranean University, a higher education institution in Greece.
  • B. UNMH
    UNMH is the primary teaching hospital of the University of New Mexico, serving as a major academic medical and trauma center for the state.
  • C. UHMD
    UHMD is the ICAO airport code for Provideniya Bay Airport in Russia’s Chukotka region.
  • D. BUMC
    BUMC is the Boston University Medical Campus, a major academic health center in Boston that integrates medical education, research, and clinical care.
  • E. UNMHA
    UNMHA is a United Nations peace mission established to monitor and support the ceasefire and redeployment of forces in Yemen’s Hudaydah region under the Stockholm Agreement.
  • 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_69d8b9ef17708190bdf7e2adbf14ddc2 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4871d43a481908aacde69bd8091b0 completed April 19, 2026, 7:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02efc9c0e88190977da0421df6b1a7 completed May 12, 2026, 9:15 a.m.
NEDg Description generation batch_6a02f08892e08190a75c4e523366feda completed May 12, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a02f18036788190ad1a2893fd104261 completed May 12, 2026, 9:23 a.m.
Created at: April 10, 2026, 10:12 a.m.