Triple
T20796319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mother (2009 film) |
E511916
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Madeo
Madeo is a 2009 South Korean thriller-drama film directed by Bong Joon-ho about a devoted mother who goes to extreme lengths to prove her son's innocence in a murder case.
|
E1451200
|
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: Madeo | Statement: [Mother (2009 film), alsoKnownAs, Madeo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madeo Context triple: [Mother (2009 film), alsoKnownAs, Madeo]
-
A.
Maribo
Maribo is a historic market town on the Danish island of Lolland, known for its cathedral and lakeside setting.
-
B.
Mindel
Mindel is a surname most notably associated with Dan Mindel, a prominent cinematographer known for his work on major Hollywood films.
-
C.
Mindel
The Mindel is a river in Bavaria, Germany, that flows through the Swabian region and ultimately drains into the Danube.
-
D.
Mayen
Mayen is a surname most notably borne by Dutch seafarer and explorer Jan Jacobszoon Mayen, after whom the Arctic island of Jan Mayen is named.
-
E.
Mayen
Mayen is a town in western Germany’s Rhineland-Palatinate region, known for its historic castle and role as a local economic and cultural center.
- 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: Madeo Triple: [Mother (2009 film), alsoKnownAs, Madeo]
Generated description
Madeo is a 2009 South Korean thriller-drama film directed by Bong Joon-ho about a devoted mother who goes to extreme lengths to prove her son's innocence in a murder case.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Madeo Target entity description: Madeo is a 2009 South Korean thriller-drama film directed by Bong Joon-ho about a devoted mother who goes to extreme lengths to prove her son's innocence in a murder case.
-
A.
Maribo
Maribo is a historic market town on the Danish island of Lolland, known for its cathedral and lakeside setting.
-
B.
Mindel
Mindel is a surname most notably associated with Dan Mindel, a prominent cinematographer known for his work on major Hollywood films.
-
C.
Mindel
The Mindel is a river in Bavaria, Germany, that flows through the Swabian region and ultimately drains into the Danube.
-
D.
Mayen
Mayen is a surname most notably borne by Dutch seafarer and explorer Jan Jacobszoon Mayen, after whom the Arctic island of Jan Mayen is named.
-
E.
Mayen
Mayen is a town in western Germany’s Rhineland-Palatinate region, known for its historic castle and role as a local economic and cultural center.
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ad6f0481909e0bab7119f10f9c |
completed | April 21, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08f8c11b348190a15c80b400486e59 |
completed | May 16, 2026, 11:07 p.m. |
| NEDg | Description generation | batch_6a08f9c9314c81909052d1a2e93d740a |
completed | May 16, 2026, 11:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08fa7040d88190b592d4f46249c2c1 |
completed | May 16, 2026, 11:14 p.m. |
Created at: April 16, 2026, 12:39 p.m.