Triple
T14198933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bacharach |
E351912
|
entity |
| Predicate | hasCityPart |
P12399
|
FINISHED |
| Object |
Medenscheid
Medenscheid is a district or locality within the historic Rhine town of Bacharach in Rhineland-Palatinate, Germany.
|
E1085051
|
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: Medenscheid | Statement: [Bacharach, hasCityPart, Medenscheid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Medenscheid Context triple: [Bacharach, hasCityPart, Medenscheid]
-
A.
Dettenschwang
Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
-
B.
Blumenstück
Blumenstück is a lyrical piano piece in D-flat major, Op. 19, by Robert Schumann, noted for its delicate, song-like character.
-
C.
Maamme
Maamme is the national anthem of Finland, known for its patriotic lyrics and prominent role in Finnish national ceremonies and sporting events.
-
D.
Marliana
Marliana is a small Italian municipality in the Tuscany region, known for its hilly landscape and historic rural character.
-
E.
Two Women
Two Women is a 1960 Italian war drama film directed by Vittorio De Sica, best known for Sophia Loren’s Oscar-winning performance as a mother struggling to protect her daughter during World War II.
- 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: Medenscheid Triple: [Bacharach, hasCityPart, Medenscheid]
Generated description
Medenscheid is a district or locality within the historic Rhine town of Bacharach in Rhineland-Palatinate, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Medenscheid Target entity description: Medenscheid is a district or locality within the historic Rhine town of Bacharach in Rhineland-Palatinate, Germany.
-
A.
Dettenschwang
Dettenschwang is a village and district of the market town Dießen am Ammersee in the Bavarian region of Germany.
-
B.
Blumenstück
Blumenstück is a lyrical piano piece in D-flat major, Op. 19, by Robert Schumann, noted for its delicate, song-like character.
-
C.
Maamme
Maamme is the national anthem of Finland, known for its patriotic lyrics and prominent role in Finnish national ceremonies and sporting events.
-
D.
Marliana
Marliana is a small Italian municipality in the Tuscany region, known for its hilly landscape and historic rural character.
-
E.
Two Women
Two Women is a 1960 Italian war drama film directed by Vittorio De Sica, best known for Sophia Loren’s Oscar-winning performance as a mother struggling to protect her daughter during World War II.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61e30f208190b61c1c7bd3501156 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd194d14008190a74021ff5a3e51d1 |
completed | May 7, 2026, 10:59 p.m. |
| NEDg | Description generation | batch_69fd1ab3f83881908113259c23fe1028 |
completed | May 7, 2026, 11:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd1b87a0c48190a68367525ed9d2cb |
completed | May 7, 2026, 11:08 p.m. |
Created at: April 10, 2026, 1:04 a.m.