Triple

T31007414
Position Surface form Disambiguated ID Type / Status
Subject Gail Dwyer E790106 entity
Predicate hasOnScreenResidence P179512 FINISHED
Object San Francisco
San Francisco is a major coastal city in Northern California known for its steep hills, iconic Golden Gate Bridge, diverse culture, and historic cable cars.
 E242 NE FINISHED

How this triple was built (3 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: San Francisco | Statement: [Gail Dwyer, hasOnScreenResidence, San Francisco]
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: San Francisco
Triple: [Gail Dwyer, hasOnScreenResidence, San Francisco]
Generated description
San Francisco is a major coastal city in Northern California known for its steep hills, iconic Golden Gate Bridge, diverse culture, and historic cable cars.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasOnScreenResidence
Context triple: [Gail Dwyer, hasOnScreenResidence, San Francisco]
  • A. hasOnScreenOccupation
    Indicates that an entity is depicted as having a particular occupation or job within an on-screen context (e.g., in a film, TV show, or other visual media).
  • B. hasOnScreenDynamic
    Indicates that one entity displays or presents another entity as a changing or interactive element on a screen.
  • C. hasResidenceTime
    Indicates the duration for which something remains or stays within a particular place, system, or state.
  • D. hasOnScreenRelative
    Indicates that one entity has a family member who appears or is depicted on screen in relation to it.
  • E. hasResidenceFeature
    Indicates that a residence possesses or is characterized by a specific feature or attribute.
  • F. None of above. chosen

Provenance (7 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_69f224c73ca48190a1e46cb58ad4045b completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f722c1bc648190a79bfdc722dcaaa4 completed May 3, 2026, 10:26 a.m.
NED1 Entity disambiguation (via context triple) batch_6a296bc3aa0881909eda108125f560f2 completed June 10, 2026, 1:50 p.m.
NEDg Description generation batch_6a296d4e7e0481908c6aa4bbfca0b520 completed June 10, 2026, 1:57 p.m.
NED2 Entity disambiguation (via description) batch_6a299ba2c2a4819090e463dcae50684f completed June 10, 2026, 5:15 p.m.
PD Predicate disambiguation batch_69f72153a9188190b02adc84e1be4af8 completed May 3, 2026, 10:20 a.m.
PDg Predicate description generation batch_69f7221bc57c819085c1464a45e61b2f completed May 3, 2026, 10:23 a.m.
Created at: April 29, 2026, 8:57 p.m.