Triple

T14773235
Position Surface form Disambiguated ID Type / Status
Subject Boyer–Moore theorem prover E347187 entity
Predicate influenced P9 FINISHED
Object NQTHM
NQTHM is an early automated theorem prover for recursive function theory and hardware/software verification, developed by Robert Boyer and J Strother Moore as a predecessor to their later Boyer–Moore theorem prover.
 E1119491 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: NQTHM | Statement: [Boyer–Moore theorem prover, influenced, NQTHM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: NQTHM
Context triple: [Boyer–Moore theorem prover, influenced, NQTHM]
  • A. NMTH
    NMTH is the acronym for the National Museum of Taiwan History, a major institution dedicated to preserving and presenting Taiwan’s historical and cultural heritage.
  • B. NQ
    NQ is the station code for North Quincy, a Massachusetts Bay Transportation Authority (MBTA) Red Line rapid transit station in Quincy, Massachusetts.
  • C. NTH
    NTH is the IATA airport code for Hokkaido Air System’s destination of Natashan Airport in Japan.
  • D. NTH
    NTH is a German alliance of technical universities in Lower Saxony that collaborate in research, teaching, and innovation.
  • E. NTH
    NTH is the National Rail station code for Neath railway station in Neath, Wales.
  • 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: NQTHM
Triple: [Boyer–Moore theorem prover, influenced, NQTHM]
Generated description
NQTHM is an early automated theorem prover for recursive function theory and hardware/software verification, developed by Robert Boyer and J Strother Moore as a predecessor to their later Boyer–Moore theorem prover.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: NQTHM
Target entity description: NQTHM is an early automated theorem prover for recursive function theory and hardware/software verification, developed by Robert Boyer and J Strother Moore as a predecessor to their later Boyer–Moore theorem prover.
  • A. NMTH
    NMTH is the acronym for the National Museum of Taiwan History, a major institution dedicated to preserving and presenting Taiwan’s historical and cultural heritage.
  • B. NQ
    NQ is the station code for North Quincy, a Massachusetts Bay Transportation Authority (MBTA) Red Line rapid transit station in Quincy, Massachusetts.
  • C. NTH
    NTH is the IATA airport code for Hokkaido Air System’s destination of Natashan Airport in Japan.
  • D. NTH
    NTH is a German alliance of technical universities in Lower Saxony that collaborate in research, teaching, and innovation.
  • E. NTH
    NTH is the National Rail station code for Neath railway station in Neath, Wales.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec81485e08190be35baafcf22b6f2 completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cfd26fc81909fba39c8705437ed completed May 8, 2026, 4:19 p.m.
NEDg Description generation batch_69fe17fc37ec8190b2e9c786a5e7843e completed May 8, 2026, 5:06 p.m.
NED2 Entity disambiguation (via description) batch_69fe18786294819080ce5ee0d8af00c9 completed May 8, 2026, 5:08 p.m.
Created at: April 10, 2026, 1:31 a.m.