Triple

T871080
Position Surface form Disambiguated ID Type / Status
Subject A24 road E18813 entity
Predicate routeNumber P1864 FINISHED
Object A24
A24 is a major road designation used in several countries, typically referring to important regional or intercity routes.
 E102285 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: A24 | Statement: [A24 road, routeNumber, A24]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: A24
Context triple: [A24 road, routeNumber, A24]
  • A. A22
    A22 is a major Portuguese motorway, commonly known as Via do Infante, that runs across the Algarve region in southern Portugal.
  • B. A44
    A44 is a major trunk road in the United Kingdom that runs across central and western England into mid Wales, linking several important towns and regions.
  • C. A40
    A40 is a major French motorway, also known as the "Autoroute Blanche," that connects Mâcon to the Mont Blanc region through the Jura and Alps.
  • D. A6
    A6 is a major German autobahn that serves as an important east–west transport corridor in southern Germany.
  • E. A2C
    A2C (Advantage Actor-Critic) is a popular synchronous policy gradient reinforcement learning algorithm that combines value-based and policy-based methods to improve training stability and efficiency.
  • 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: A24
Triple: [A24 road, routeNumber, A24]
Generated description
A24 is a major road designation used in several countries, typically referring to important regional or intercity routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: A24
Target entity description: A24 is a major road designation used in several countries, typically referring to important regional or intercity routes.
  • A. A22
    A22 is a major Portuguese motorway, commonly known as Via do Infante, that runs across the Algarve region in southern Portugal.
  • B. A44
    A44 is a major trunk road in the United Kingdom that runs across central and western England into mid Wales, linking several important towns and regions.
  • C. A40
    A40 is a major French motorway, also known as the "Autoroute Blanche," that connects Mâcon to the Mont Blanc region through the Jura and Alps.
  • D. A6
    A6 is a major German autobahn that serves as an important east–west transport corridor in southern Germany.
  • E. A2C
    A2C (Advantage Actor-Critic) is a popular synchronous policy gradient reinforcement learning algorithm that combines value-based and policy-based methods to improve training stability and efficiency.
  • 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_69a4938db1f081909bcd1ad2713b6096 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac94d5ac81909feee876696da589 completed March 1, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3cb9a648190981182add42325f3 completed March 4, 2026, 3:15 a.m.
NEDg Description generation batch_69a7a558c1308190810a139ad24dfc9d completed March 4, 2026, 3:22 a.m.
NED2 Entity disambiguation (via description) batch_69a7a5e744188190ae30544753fb9399 completed March 4, 2026, 3:24 a.m.
Created at: March 1, 2026, 7:39 p.m.