Triple
T29204983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eric Tsang |
E740385
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Mad World
Mad World is a 2016 Hong Kong drama film that sensitively portrays mental illness and family relationships, directed by Wong Chun and acclaimed for its performances and social realism.
|
E1856475
|
NE FINISHED |
How this triple was built (2 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: Mad World | Statement: [Eric Tsang, notableWork, Mad World]
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: Mad World Triple: [Eric Tsang, notableWork, Mad World]
Generated description
Mad World is a 2016 Hong Kong drama film that sensitively portrays mental illness and family relationships, directed by Wong Chun and acclaimed for its performances and social realism.
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_69f07cb974108190b7e86ca489a6ebb6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f663c8800c819096adc9588d261b96 |
completed | May 2, 2026, 8:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2569c0bd408190b1d765adf9248210 |
completed | June 7, 2026, 12:53 p.m. |
| NEDg | Description generation | batch_6a256de41c4481909176bfe24f1e4fe8 |
completed | June 7, 2026, 1:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a25724ed7588190862ceef339305f35 |
completed | June 7, 2026, 1:29 p.m. |
Created at: April 28, 2026, 12:08 p.m.