Triple

T673631
Position Surface form Disambiguated ID Type / Status
Subject Division of Professional Relations E13029 entity
Predicate hasAbbreviation P43 FINISHED
Object PROF
PROF is the standard abbreviation for the Division of Professional Relations, an organizational unit focused on issues affecting professional practice and workplace relations.
 E83723 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: PROF | Statement: [Division of Professional Relations, hasAbbreviation, PROF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PROF
Context triple: [Division of Professional Relations, hasAbbreviation, PROF]
  • A. Pol
    Pol is a given name and variant of Paul, used in several European languages such as Catalan and French.
  • B. Pe
    Pe is a Hebrew consonant letter that represents a "p" or "f" sound and has both standard and final written forms.
  • C. PJ
    PJ is a musical artist known for contributing guest performances to other musicians’ tracks.
  • D. PRTC
    PRTC is a public transit agency serving Northern Virginia with commuter and local bus services.
  • E. Repre
    Repre is the popular nickname for the Slovakia men's national ice hockey team, used by fans and media to refer to the national squad.
  • 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: PROF
Triple: [Division of Professional Relations, hasAbbreviation, PROF]
Generated description
PROF is the standard abbreviation for the Division of Professional Relations, an organizational unit focused on issues affecting professional practice and workplace relations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PROF
Target entity description: PROF is the standard abbreviation for the Division of Professional Relations, an organizational unit focused on issues affecting professional practice and workplace relations.
  • A. Pol
    Pol is a given name and variant of Paul, used in several European languages such as Catalan and French.
  • B. Pe
    Pe is a Hebrew consonant letter that represents a "p" or "f" sound and has both standard and final written forms.
  • C. PJ
    PJ is a musical artist known for contributing guest performances to other musicians’ tracks.
  • D. PRTC
    PRTC is a public transit agency serving Northern Virginia with commuter and local bus services.
  • E. Repre
    Repre is the popular nickname for the Slovakia men's national ice hockey team, used by fans and media to refer to the national squad.
  • 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_69a4933d3bf88190972041cd8cf143b9 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a02537d08190942ee5fc8c50610a completed March 1, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c39f3e1481908f395cdb19cfd2fc completed March 2, 2026, 5:06 p.m.
NEDg Description generation batch_69a5c42d4a0481908af4a71b4625c130 completed March 2, 2026, 5:09 p.m.
NED2 Entity disambiguation (via description) batch_69a5ce16df088190850a3b851558eba8 completed March 2, 2026, 5:51 p.m.
Created at: March 1, 2026, 7:36 p.m.