Triple

T17301930
Position Surface form Disambiguated ID Type / Status
Subject Pete E420058 entity
Predicate associatedWithCharacter P1481 FINISHED
Object Lampie
Lampie is a supporting character known as the kindly but often inebriated lighthouse keeper in Disney’s film "Pete’s Dragon."
 E1261191 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: Lampie | Statement: [Pete, associatedWithCharacter, Lampie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lampie
Context triple: [Pete, associatedWithCharacter, Lampie]
  • A. Lumo
    Lumo is a British open-access train operator running low-cost, long-distance electric services on the East Coast Main Line between London and northeastern England.
  • B. Lampione
    Lampione is a tiny, uninhabited rocky islet in the Mediterranean Sea, part of Italy’s Pelagie Islands and known for its rich marine life and popular diving spots.
  • C. Lumi
    Lumi is a feminine given name used in various cultures, often associated with meanings related to light or snow.
  • D. Lampa
    Lampa is a commune and town in central Chile known for its semi-rural character and growing residential and industrial development near Santiago.
  • E. Lampa
    Lampa is a town in southern Peru that serves as the capital of Lampa Province in the Puno Region, known for its colonial architecture and historic churches.
  • 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: Lampie
Triple: [Pete, associatedWithCharacter, Lampie]
Generated description
Lampie is a supporting character known as the kindly but often inebriated lighthouse keeper in Disney’s film "Pete’s Dragon."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lampie
Target entity description: Lampie is a supporting character known as the kindly but often inebriated lighthouse keeper in Disney’s film "Pete’s Dragon."
  • A. Lumo
    Lumo is a British open-access train operator running low-cost, long-distance electric services on the East Coast Main Line between London and northeastern England.
  • B. Lampione
    Lampione is a tiny, uninhabited rocky islet in the Mediterranean Sea, part of Italy’s Pelagie Islands and known for its rich marine life and popular diving spots.
  • C. Lumi
    Lumi is a feminine given name used in various cultures, often associated with meanings related to light or snow.
  • D. Lampa
    Lampa is a commune and town in central Chile known for its semi-rural character and growing residential and industrial development near Santiago.
  • E. Lampa
    Lampa is a town in southern Peru that serves as the capital of Lampa Province in the Puno Region, known for its colonial architecture and historic churches.
  • 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_69d886db32608190a61e18862c5a8af6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e438fae8848190a06c5866e606baac completed April 19, 2026, 2:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0180dc929c819096a7a5dc81e5b6ef completed May 11, 2026, 7:10 a.m.
NEDg Description generation batch_6a0181ae2d588190a4ff68094529a994 completed May 11, 2026, 7:13 a.m.
NED2 Entity disambiguation (via description) batch_6a0182ccd104819088569cf0be87b4d3 completed May 11, 2026, 7:18 a.m.
Created at: April 10, 2026, 5:41 a.m.