Triple

T11466140
Position Surface form Disambiguated ID Type / Status
Subject WALL·E (film score) E271783 entity
Predicate hasTrack P3284 FINISHED
Object BNL
BNL is a track from the WALL·E film score, associated with the fictional megacorporation Buy n Large featured prominently in the movie.
E928020 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: BNL | Statement: [WALL·E (film score), hasTrack, BNL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BNL
Context triple: [WALL·E (film score), hasTrack, BNL]
  • A. BNL
    BNL is a public high school in Bedford, Indiana, known for its academic programs and competitive athletics, particularly in basketball.
  • B. BNL E852
    BNL E852 was a high-energy physics experiment at Brookhaven National Laboratory focused on studying meson spectroscopy and exotic hadronic states using a high-intensity pion beam.
  • C. BNX
    BNX is the IATA airport code for Banja Luka International Airport in Bosnia and Herzegovina.
  • D. BNP
    BNP is the stock ticker symbol for BNP Paribas, a major French international banking and financial services group.
  • E. BNP
    BNP is the National Rail station code assigned to Barnstaple railway station in Devon, England.
  • 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: BNL
Triple: [WALL·E (film score), hasTrack, BNL]
Generated description
BNL is a track from the WALL·E film score, associated with the fictional megacorporation Buy n Large featured prominently in the movie.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: BNL
Target entity description: BNL is a track from the WALL·E film score, associated with the fictional megacorporation Buy n Large featured prominently in the movie.
  • A. BNL
    BNL is a public high school in Bedford, Indiana, known for its academic programs and competitive athletics, particularly in basketball.
  • B. BNL E852
    BNL E852 was a high-energy physics experiment at Brookhaven National Laboratory focused on studying meson spectroscopy and exotic hadronic states using a high-intensity pion beam.
  • C. BNX
    BNX is the IATA airport code for Banja Luka International Airport in Bosnia and Herzegovina.
  • D. BNP
    BNP is the stock ticker symbol for BNP Paribas, a major French international banking and financial services group.
  • E. BNP
    BNP is the National Rail station code assigned to Barnstaple railway station in Devon, England.
  • 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_69d6aae0c8d881908a5a360c0be3242e completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d822f5eb988190b309b8e309f6d1a5 completed April 9, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e9377ff4819096971a77e0181eaf completed April 20, 2026, 8:52 a.m.
NEDg Description generation batch_69e5f1593c2c8190885f80ad5eeba3ec completed April 20, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69e5f87bbd988190ac388a3c34b2e95a completed April 20, 2026, 9:57 a.m.
Created at: April 8, 2026, 9:35 p.m.