Triple

T10329652
Position Surface form Disambiguated ID Type / Status
Subject Acid3 E242842 entity
Predicate testsFeature P5348 FINISHED
Object SMIL
SMIL (Synchronized Multimedia Integration Language) is a W3C XML-based markup language designed to synchronize and integrate multimedia elements such as audio, video, and text for interactive presentations on the web.
 E856246 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: SMIL | Statement: [Acid3, testsFeature, SMIL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SMIL
Context triple: [Acid3, testsFeature, SMIL]
  • A. WML
    WML is the National Rail station code for Wilmslow railway station in Cheshire, England.
  • B. SMI
    SMI is the Swiss Market Index, a leading stock market index that tracks the performance of major blue-chip companies listed on the SIX Swiss Exchange.
  • C. SMI
    SMI is the IATA airport code for Samos International Airport, the main air gateway to the Greek island of Samos.
  • D. SMI
    SMI is the vehicle registration code assigned to the town of Mikołów in Poland.
  • E. TVML
    TVML is Apple’s XML-based markup language used to define the user interface and layout of tvOS apps built with TVMLKit.
  • 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: SMIL
Triple: [Acid3, testsFeature, SMIL]
Generated description
SMIL (Synchronized Multimedia Integration Language) is a W3C XML-based markup language designed to synchronize and integrate multimedia elements such as audio, video, and text for interactive presentations on the web.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SMIL
Target entity description: SMIL (Synchronized Multimedia Integration Language) is a W3C XML-based markup language designed to synchronize and integrate multimedia elements such as audio, video, and text for interactive presentations on the web.
  • A. WML
    WML is the National Rail station code for Wilmslow railway station in Cheshire, England.
  • B. SMI
    SMI is the Swiss Market Index, a leading stock market index that tracks the performance of major blue-chip companies listed on the SIX Swiss Exchange.
  • C. SMI
    SMI is the IATA airport code for Samos International Airport, the main air gateway to the Greek island of Samos.
  • D. SMI
    SMI is the vehicle registration code assigned to the town of Mikołów in Poland.
  • E. TVML
    TVML is Apple’s XML-based markup language used to define the user interface and layout of tvOS apps built with TVMLKit.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4dfc1b0488190ac04da58a4987da0 completed April 7, 2026, 10:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69d71db4a8dc81909adb2a044e74fd6b completed April 9, 2026, 3:32 a.m.
NEDg Description generation batch_69d73189d7cc8190b81bb30994b3900f completed April 9, 2026, 4:56 a.m.
NED2 Entity disambiguation (via description) batch_69d7329891688190b5c1ec5906728f01 completed April 9, 2026, 5:01 a.m.
Created at: April 6, 2026, 11:52 a.m.