Triple

T9626335
Position Surface form Disambiguated ID Type / Status
Subject Los Angeles Bureau of Sanitation E232474 entity
Predicate alsoKnownAs P39 FINISHED
Object LASAN
LASAN is the public agency responsible for managing wastewater, solid waste, and environmental services for the City of Los Angeles.
 E809873 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: LASAN | Statement: [Los Angeles Bureau of Sanitation, alsoKnownAs, LASAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LASAN
Context triple: [Los Angeles Bureau of Sanitation, alsoKnownAs, LASAN]
  • A. Lasn
    Lasn is the surname of Kalle Lasn, an Estonian-Canadian activist, author, and co-founder of the anti-consumerist magazine Adbusters.
  • B. LASK
    LASK is a professional Austrian football club based in Linz that competes in the Austrian Bundesliga.
  • C. Lasi
    Lasi is a regional dialect of the Sindhi language spoken primarily in parts of Balochistan and Sindh in Pakistan.
  • D. Lavasan
    Lavasan is a wealthy suburban town northeast of Tehran, Iran, known for its villas, mild climate, and popularity as a weekend retreat for the capital’s residents.
  • E. LAJ
    LAJ is the station code for La Junta station, an Amtrak railroad stop in La Junta, Colorado, serving long-distance passenger trains.
  • 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: LASAN
Triple: [Los Angeles Bureau of Sanitation, alsoKnownAs, LASAN]
Generated description
LASAN is the public agency responsible for managing wastewater, solid waste, and environmental services for the City of Los Angeles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LASAN
Target entity description: LASAN is the public agency responsible for managing wastewater, solid waste, and environmental services for the City of Los Angeles.
  • A. Lasn
    Lasn is the surname of Kalle Lasn, an Estonian-Canadian activist, author, and co-founder of the anti-consumerist magazine Adbusters.
  • B. LASK
    LASK is a professional Austrian football club based in Linz that competes in the Austrian Bundesliga.
  • C. Lasi
    Lasi is a regional dialect of the Sindhi language spoken primarily in parts of Balochistan and Sindh in Pakistan.
  • D. Lavasan
    Lavasan is a wealthy suburban town northeast of Tehran, Iran, known for its villas, mild climate, and popularity as a weekend retreat for the capital’s residents.
  • E. LAJ
    LAJ is the station code for La Junta station, an Amtrak railroad stop in La Junta, Colorado, serving long-distance passenger trains.
  • 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_69ca848793ec8190a93a12383a754dc0 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9afc9144819084b208c3d04174ba completed April 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1798129dc819090a29efcbcf34b8e completed April 4, 2026, 8:50 p.m.
NEDg Description generation batch_69d17a48fa188190af50e4a1b9f1653b completed April 4, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_69d17ac217188190921d08f8aa7ca833 completed April 4, 2026, 8:55 p.m.
Created at: March 30, 2026, 8:10 p.m.