Triple

T571930
Position Surface form Disambiguated ID Type / Status
Subject Massachusetts Route 16 E13680 entity
Predicate hasRouteDesignation P5539 FINISHED
Object MA 16
MA 16 is a state highway designation for a major east–west route in Massachusetts that passes through several cities and suburbs near Boston.
 E71599 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: MA 16 | Statement: [Massachusetts Route 16, hasRouteDesignation, MA 16]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MA 16
Context triple: [Massachusetts Route 16, hasRouteDesignation, MA 16]
  • A. MHA
    MHA is the commonly used abbreviation for India’s Ministry of Home Affairs, the central government ministry responsible for internal security, domestic policy, and law and order.
  • B. MA
    MA is the two-letter ISO 3166-1 alpha-2 country code assigned to Morocco.
  • C. Ma
    Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
  • D. MAA
    MAA is the acronym for the Maryland Aviation Administration, the state agency that oversees and manages Maryland’s public-use airports, including Baltimore/Washington International Thurgood Marshall Airport.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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: MA 16
Triple: [Massachusetts Route 16, hasRouteDesignation, MA 16]
Generated description
MA 16 is a state highway designation for a major east–west route in Massachusetts that passes through several cities and suburbs near Boston.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MA 16
Target entity description: MA 16 is a state highway designation for a major east–west route in Massachusetts that passes through several cities and suburbs near Boston.
  • A. MHA
    MHA is the commonly used abbreviation for India’s Ministry of Home Affairs, the central government ministry responsible for internal security, domestic policy, and law and order.
  • B. MA
    MA is the two-letter ISO 3166-1 alpha-2 country code assigned to Morocco.
  • C. Ma
    Ma is a common Chinese surname borne by many notable individuals across fields such as music, politics, and sports.
  • D. MAA
    MAA is the acronym for the Maryland Aviation Administration, the state agency that oversees and manages Maryland’s public-use airports, including Baltimore/Washington International Thurgood Marshall Airport.
  • E. MAB
    MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b49bad88190bc73d31a317c0ef4 completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4fc8475d881909e80d60fbb50c271 completed March 2, 2026, 2:57 a.m.
NEDg Description generation batch_69a4fd2938288190b00cb79a10d91150 completed March 2, 2026, 2:59 a.m.
NED2 Entity disambiguation (via description) batch_69a4fdb5f42c81908ce753cd4283962c completed March 2, 2026, 3:02 a.m.
Created at: March 1, 2026, 7:33 p.m.