Triple
T10248412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mathieu Amalric |
E240278
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Munich
"Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
|
E854833
|
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: Munich | Statement: [Mathieu Amalric, notableWork, Munich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munich Context triple: [Mathieu Amalric, notableWork, Munich]
-
A.
Munich
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
B.
Leverkusen
Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
-
C.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
D.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
E.
Nuremberg
Nuremberg is a historic city in Bavaria, Germany, known for its medieval architecture and its role as the site of the post–World War II war crimes tribunals.
- 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: Munich Triple: [Mathieu Amalric, notableWork, Munich]
Generated description
"Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Munich Target entity description: "Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
-
A.
Munich
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
B.
Leverkusen
Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
-
C.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
-
D.
Ingolstadt
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
E.
Nuremberg
Nuremberg is a historic city in Bavaria, Germany, known for its medieval architecture and its role as the site of the post–World War II war crimes tribunals.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71cbd67648190ba7faebd12d96ca9 |
completed | April 9, 2026, 3:27 a.m. |
| NEDg | Description generation | batch_69d73180d90481908f1b4768230edd36 |
completed | April 9, 2026, 4:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7326b14988190bff33dc01e690707 |
completed | April 9, 2026, 5 a.m. |
Created at: April 6, 2026, 11:27 a.m.