Triple

T8563879
Position Surface form Disambiguated ID Type / Status
Subject San Luis Obispo E202755 entity
Predicate nickname P55 FINISHED
Object SLO
SLO is a common abbreviation for San Luis Obispo, a small coastal city in California known for its historic mission, vibrant downtown, and proximity to beaches and wine country.
 E743584 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: SLO | Statement: [San Luis Obispo, nickname, SLO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SLO
Context triple: [San Luis Obispo, nickname, SLO]
  • A. SLO
    SLO is the three-letter International Olympic Committee country code representing Slovenia in Olympic competitions.
  • B. Slocene
    Slocene is a river in Latvia that serves as one of the tributaries feeding into the larger Lielupe River system.
  • C. San Jo
    San Jo is an informal nickname commonly used to refer to the city of San Jose, California.
  • D. Ojai
    Ojai is a small, scenic city in Southern California known for its arts community, boutique tourism, and surrounding mountains and orange groves.
  • E. La Elipa
    La Elipa is a Madrid Metro station in the Ciudad Lineal district, serving the residential neighborhood of the same name on Line 2.
  • 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: SLO
Triple: [San Luis Obispo, nickname, SLO]
Generated description
SLO is a common abbreviation for San Luis Obispo, a small coastal city in California known for its historic mission, vibrant downtown, and proximity to beaches and wine country.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SLO
Target entity description: SLO is a common abbreviation for San Luis Obispo, a small coastal city in California known for its historic mission, vibrant downtown, and proximity to beaches and wine country.
  • A. SLO
    SLO is the three-letter International Olympic Committee country code representing Slovenia in Olympic competitions.
  • B. Slocene
    Slocene is a river in Latvia that serves as one of the tributaries feeding into the larger Lielupe River system.
  • C. San Jo
    San Jo is an informal nickname commonly used to refer to the city of San Jose, California.
  • D. Ojai
    Ojai is a small, scenic city in Southern California known for its arts community, boutique tourism, and surrounding mountains and orange groves.
  • E. La Elipa
    La Elipa is a Madrid Metro station in the Ciudad Lineal district, serving the residential neighborhood of the same name on Line 2.
  • 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_69ca8326e6c881908ff720d6abaebdc5 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe9d11274819099cc33a21a993a1f completed March 31, 2026, 3:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce895f91bc819099b1b2df59374403 completed April 2, 2026, 3:21 p.m.
NEDg Description generation batch_69ce8cc19b8c81909c89e771fdb7b043 completed April 2, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_69ce8d595d80819093a1b849bcb3c7c7 completed April 2, 2026, 3:38 p.m.
Created at: March 30, 2026, 6:20 p.m.