Triple

T24870734
Position Surface form Disambiguated ID Type / Status
Subject Lanchester’s laws of combat E622417 entity
Predicate hasPart P35 FINISHED
Object Lanchester’s linear law
Lanchester’s linear law is a mathematical model of combat that describes how the fighting strength of forces using aimed fire scales linearly with their size, making it useful for analyzing attrition in modern warfare.
E622417 NE FINISHED

How this triple was built (2 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: Lanchester’s linear law | Statement: [Lanchester’s laws of combat, hasPart, Lanchester’s linear law]
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: Lanchester’s linear law
Triple: [Lanchester’s laws of combat, hasPart, Lanchester’s linear law]
Generated description
Lanchester’s linear law is a mathematical model of combat that describes how the fighting strength of forces using aimed fire scales linearly with their size, making it useful for analyzing attrition in modern warfare.

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_69e2fac3fdbc81909c2ec49be5743cd9 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f4230819248190a631eed2a0b116ce completed May 1, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1075ae855881908a679f6db7687e83 completed May 22, 2026, 3:26 p.m.
NEDg Description generation batch_6a10765abfb881908ab8908e1e497f64 completed May 22, 2026, 3:29 p.m.
NED2 Entity disambiguation (via description) batch_6a107735ae30819095bf24d523279c69 completed May 22, 2026, 3:33 p.m.
Created at: April 18, 2026, 5:23 a.m.