Triple

T5782877
Position Surface form Disambiguated ID Type / Status
Subject Coming Home E128201 entity
Predicate mainCharacter P1183 FINISHED
Object Dan Dan
Dan Dan is the central protagonist of the film "Coming Home," whose personal journey and experiences drive the emotional core of the story.
E543515 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: Dan Dan | Statement: [Coming Home, mainCharacter, Dan Dan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dan Dan
Context triple: [Coming Home, mainCharacter, Dan Dan]
  • A. Daksum
    Daksum is a scenic hill station and forested valley in Jammu and Kashmir, India, known for its lush landscapes, trout-filled streams, and trekking routes in the Anantnag region.
  • B. Din Da Da
    "Din Da Da" is a percussive, chant-driven electro track originally by George Kranz that has been widely sampled and covered in hip-hop and dance music.
  • C. Ramenki
    Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
  • D. Maoke Plate
    The Maoke Plate is a minor tectonic plate in the region of New Guinea, contributing to the complex and active plate boundary interactions in eastern Indonesia.
  • E. Oshiage
    Oshiage is a district in Sumida, Tokyo, best known as the location of the Tokyo Skytree and its surrounding commercial complex.
  • 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: Dan Dan
Triple: [Coming Home, mainCharacter, Dan Dan]
Generated description
Dan Dan is the central protagonist of the film "Coming Home," whose personal journey and experiences drive the emotional core of the story.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dan Dan
Target entity description: Dan Dan is the central protagonist of the film "Coming Home," whose personal journey and experiences drive the emotional core of the story.
  • A. Daksum
    Daksum is a scenic hill station and forested valley in Jammu and Kashmir, India, known for its lush landscapes, trout-filled streams, and trekking routes in the Anantnag region.
  • B. Din Da Da
    "Din Da Da" is a percussive, chant-driven electro track originally by George Kranz that has been widely sampled and covered in hip-hop and dance music.
  • C. Ramenki
    Ramenki is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Ramenki District of western Moscow, Russia.
  • D. Maoke Plate
    The Maoke Plate is a minor tectonic plate in the region of New Guinea, contributing to the complex and active plate boundary interactions in eastern Indonesia.
  • E. Oshiage
    Oshiage is a district in Sumida, Tokyo, best known as the location of the Tokyo Skytree and its surrounding commercial complex.
  • 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_69c0084450048190bc647b649a05136b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02a184870819084251554eae1e33c completed March 22, 2026, 5:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e7a1cb88190980e0cf675aaa906 completed March 22, 2026, 11:42 p.m.
NEDg Description generation batch_69c086b2f5888190a986efaf948b25fb completed March 23, 2026, 12:17 a.m.
NED2 Entity disambiguation (via description) batch_69c087493f808190bec82872e44af85c completed March 23, 2026, 12:20 a.m.
Created at: March 22, 2026, 3:50 p.m.