Triple

T3853432
Position Surface form Disambiguated ID Type / Status
Subject Paul Valéry University Montpellier 3 E85352 entity
Predicate hasAlternativeName P39 FINISHED
Object UPVM3
UPVM3 is a French public university in Montpellier specializing in arts, humanities, and social sciences, named after the writer and philosopher Paul Valéry.
 E392504 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: UPVM3 | Statement: [Paul Valéry University Montpellier 3, hasAlternativeName, UPVM3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPVM3
Context triple: [Paul Valéry University Montpellier 3, hasAlternativeName, UPVM3]
  • A. VMU
    The VMU (Visual Memory Unit) is a memory card and secondary screen accessory for the Sega Dreamcast that provides game save storage and mini-game functionality.
  • B. UP-32
    UP-32 is the vehicle registration code assigned to motor vehicles registered in the city of Lucknow, Uttar Pradesh, India.
  • C. FVMV
    FVMV is the ICAO airport code for Masvingo Airport in Masvingo, Zimbabwe.
  • D. U3
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • E. LVM3
    LVM3 is India’s heavy-lift launch vehicle developed by ISRO to carry large communication and deep-space satellites into orbit.
  • 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: UPVM3
Triple: [Paul Valéry University Montpellier 3, hasAlternativeName, UPVM3]
Generated description
UPVM3 is a French public university in Montpellier specializing in arts, humanities, and social sciences, named after the writer and philosopher Paul Valéry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPVM3
Target entity description: UPVM3 is a French public university in Montpellier specializing in arts, humanities, and social sciences, named after the writer and philosopher Paul Valéry.
  • A. VMU
    The VMU (Visual Memory Unit) is a memory card and secondary screen accessory for the Sega Dreamcast that provides game save storage and mini-game functionality.
  • B. UP-32
    UP-32 is the vehicle registration code assigned to motor vehicles registered in the city of Lucknow, Uttar Pradesh, India.
  • C. FVMV
    FVMV is the ICAO airport code for Masvingo Airport in Masvingo, Zimbabwe.
  • D. U3
    U3 is one of the main lines of the Nuremberg U-Bahn rapid transit system in Nuremberg, Germany.
  • E. LVM3
    LVM3 is India’s heavy-lift launch vehicle developed by ISRO to carry large communication and deep-space satellites into orbit.
  • 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_69aed936de1c81908f91bed80f70abb2 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec0438308190865ff74bee5a1cf2 completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5041c7250819093b2743afeb6e36c completed March 14, 2026, 6:45 a.m.
NEDg Description generation batch_69b504c46dcc8190a9775c39e5c734a9 completed March 14, 2026, 6:48 a.m.
NED2 Entity disambiguation (via description) batch_69b505742830819093a861bde17c03c0 completed March 14, 2026, 6:51 a.m.
Created at: March 9, 2026, 3:19 p.m.