Triple

T15265527
Position Surface form Disambiguated ID Type / Status
Subject Conflent E364891 entity
Predicate containsTown P847 FINISHED
Object Codalet
Codalet is a small commune in the Pyrénées-Orientales department of southern France, known for its picturesque setting in the historic Conflent region.
E1147495 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: Codalet | Statement: [Conflent, containsTown, Codalet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Codalet
Context triple: [Conflent, containsTown, Codalet]
  • A. Consiva
    Consiva is a company or brand name, likely associated with operations or business services under the label "Ops Consiva."
  • B. IronWolf
    IronWolf is Seagate’s line of hard drives designed specifically for high-capacity, always-on network-attached storage (NAS) systems.
  • C. Trilogy Systems
    Trilogy Systems was a pioneering but ultimately unsuccessful 1980s startup founded by computer architect Gene Amdahl to develop advanced mainframe computers using cutting-edge semiconductor technology.
  • D. Zyvex
    Zyvex is a pioneering nanotechnology company known for its early work in molecular nanotechnology and advanced manufacturing.
  • E. Fordata
    Fordata is one of the islands in Indonesia’s Tanimbar archipelago, located in the Maluku province.
  • 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: Codalet
Triple: [Conflent, containsTown, Codalet]
Generated description
Codalet is a small commune in the Pyrénées-Orientales department of southern France, known for its picturesque setting in the historic Conflent region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Codalet
Target entity description: Codalet is a small commune in the Pyrénées-Orientales department of southern France, known for its picturesque setting in the historic Conflent region.
  • A. Consiva
    Consiva is a company or brand name, likely associated with operations or business services under the label "Ops Consiva."
  • B. IronWolf
    IronWolf is Seagate’s line of hard drives designed specifically for high-capacity, always-on network-attached storage (NAS) systems.
  • C. Trilogy Systems
    Trilogy Systems was a pioneering but ultimately unsuccessful 1980s startup founded by computer architect Gene Amdahl to develop advanced mainframe computers using cutting-edge semiconductor technology.
  • D. Zyvex
    Zyvex is a pioneering nanotechnology company known for its early work in molecular nanotechnology and advanced manufacturing.
  • E. Fordata
    Fordata is one of the islands in Indonesia’s Tanimbar archipelago, located in the Maluku province.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00851c5b88190a296b6a105d3ee30 completed April 15, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee600340c8190a1888d35c2c1bc86 completed May 9, 2026, 7:45 a.m.
NEDg Description generation batch_69fee714cf6c81908dc4427590eeae85 completed May 9, 2026, 7:49 a.m.
NED2 Entity disambiguation (via description) batch_69feeae4731081909964bd8b1ea3dd7a completed May 9, 2026, 8:05 a.m.
Created at: April 10, 2026, 3:14 a.m.