Triple

T2830380
Position Surface form Disambiguated ID Type / Status
Subject Homs E62221 entity
Predicate alternativeName P39 FINISHED
Object Emesa
Emesa is the ancient name of the Syrian city now known as Homs, historically significant as a religious and trading center in Roman and early Christian times.
E302263 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: Emesa | Statement: [Homs, alternativeName, Emesa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Emesa
Context triple: [Homs, alternativeName, Emesa]
  • A. Huraymila
    Huraymila is a small town in central Saudi Arabia known for its traditional character and location within the greater Riyadh region.
  • B. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • C. Tirhuta
    Tirhuta is a historical Brahmic script traditionally used for writing the Maithili language in the Mithila region of India and Nepal.
  • D. Menetes
    Menetes is a genus of rodents in the squirrel family, comprising ground-dwelling squirrels native to parts of Asia.
  • E. Mauregard
    Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
  • 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: Emesa
Triple: [Homs, alternativeName, Emesa]
Generated description
Emesa is the ancient name of the Syrian city now known as Homs, historically significant as a religious and trading center in Roman and early Christian times.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Emesa
Target entity description: Emesa is the ancient name of the Syrian city now known as Homs, historically significant as a religious and trading center in Roman and early Christian times.
  • A. Huraymila
    Huraymila is a small town in central Saudi Arabia known for its traditional character and location within the greater Riyadh region.
  • B. Temara
    Temara is a coastal city in northwestern Morocco, situated just south of Rabat and known for its beaches and growing residential and industrial areas.
  • C. Tirhuta
    Tirhuta is a historical Brahmic script traditionally used for writing the Maithili language in the Mithila region of India and Nepal.
  • D. Menetes
    Menetes is a genus of rodents in the squirrel family, comprising ground-dwelling squirrels native to parts of Asia.
  • E. Mauregard
    Mauregard is a small commune in the Seine-et-Marne department of the Île-de-France region in north-central France, situated near Paris Charles de Gaulle Airport.
  • 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_69ab4c3c39188190955b9c49d98463d8 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdebbde4881908dfa78e28c7018e0 completed March 7, 2026, 8:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69afceb771d48190a1467a6e58f756ad completed March 10, 2026, 7:56 a.m.
NEDg Description generation batch_69afd21bf66481909d0416cf591fc2cf completed March 10, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_69afd28839a881909c758e2ea202242a completed March 10, 2026, 8:12 a.m.
Created at: March 6, 2026, 10:01 p.m.