Triple

T15497280
Position Surface form Disambiguated ID Type / Status
Subject Mark Dacascos E378850 entity
Predicate givenName P17 FINISHED
Object Mark
Mark is a common masculine given name used in many English-speaking and European countries, derived from the Latin name Marcus.
E161211 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: Mark | Statement: [Mark Dacascos, givenName, Mark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark
Context triple: [Mark Dacascos, givenName, Mark]
  • A. Mark
    The Mark was the basic unit of currency used in Germany during various historical periods, including the era of the Papiermark.
  • B. Mark
    Mark is a quirky, music-obsessed employee at the independent record store in the 1995 cult film "Empire Records," known for his goofy charm and laid-back attitude.
  • C. Mark
    Mark is a punctuation symbol used in writing systems, including those that employ the Cyrillic Extended-B Unicode block.
  • D. Mark
    Mark is the introspective, emotionally detached young man who returns to his New Jersey hometown and undergoes a journey of self-discovery in the film "Garden State."
  • E. Mark
    Mark is a character in the horror film "Midsommar," one of the American friends who travels to a remote Swedish commune and becomes entangled in its disturbing pagan rituals.
  • 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: Mark
Triple: [Mark Dacascos, givenName, Mark]
Generated description
Mark is a common masculine given name used in many English-speaking and European countries, derived from the Latin name Marcus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mark
Target entity description: Mark is a common masculine given name used in many English-speaking and European countries, derived from the Latin name Marcus.
  • A. Mark chosen
    Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
  • B. Mark
    Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
  • C. Mark
    Mark is the given name of filmmaker Mark Neveldine, known for co-directing high-energy action films like the "Crank" series.
  • D. Mark
    Mark is the real first name of The Undertaker, the legendary professional wrestler best known for his long-running career in WWE.
  • E. Mark
    Mark is the first name of Mark Cuban, the American billionaire entrepreneur and owner of the NBA’s Dallas Mavericks.
  • F. None of above.

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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fb0aee081909db1c54349ec8492 completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3665769c8190be1af51a82a5e75f completed May 9, 2026, 1:28 p.m.
NEDg Description generation batch_69ff37ac082c81908e057c2d61a79cf0 completed May 9, 2026, 1:33 p.m.
NED2 Entity disambiguation (via description) batch_69ff384962f88190964fc040a2a44aa8 completed May 9, 2026, 1:36 p.m.
Created at: April 10, 2026, 3:53 a.m.