Triple
T9833934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marburg-Biedenkopf |
E239053
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object |
Angelburg
Angelburg is a small municipality in the Marburg-Biedenkopf district in the state of Hesse, central Germany.
|
E830456
|
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: Angelburg | Statement: [Marburg-Biedenkopf, containsMunicipality, Angelburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Angelburg Context triple: [Marburg-Biedenkopf, containsMunicipality, Angelburg]
-
A.
Offenberg
Offenberg is a municipality in the Bavarian region of Germany, situated within the Regen district.
-
B.
Arzdorf
Arzdorf is a village and district of the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
C.
Vöcklabruck
Vöcklabruck is a small historic town in Upper Austria known as a regional center near the Attersee lake and the foothills of the Alps.
-
D.
Wörgl
Wörgl is a small Austrian town in the state of Tyrol, known for its role in early 20th-century economic experiments with local currency and its location in the Inn Valley near major Alpine ski areas.
-
E.
Leoben
Leoben is a historic industrial and university city in the Austrian state of Styria, known especially for its steel industry and mining university.
- 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: Angelburg Triple: [Marburg-Biedenkopf, containsMunicipality, Angelburg]
Generated description
Angelburg is a small municipality in the Marburg-Biedenkopf district in the state of Hesse, central Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Angelburg Target entity description: Angelburg is a small municipality in the Marburg-Biedenkopf district in the state of Hesse, central Germany.
-
A.
Offenberg
Offenberg is a municipality in the Bavarian region of Germany, situated within the Regen district.
-
B.
Arzdorf
Arzdorf is a village and district of the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
-
C.
Vöcklabruck
Vöcklabruck is a small historic town in Upper Austria known as a regional center near the Attersee lake and the foothills of the Alps.
-
D.
Wörgl
Wörgl is a small Austrian town in the state of Tyrol, known for its role in early 20th-century economic experiments with local currency and its location in the Inn Valley near major Alpine ski areas.
-
E.
Leoben
Leoben is a historic industrial and university city in the Austrian state of Styria, known especially for its steel industry and mining university.
- 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3385054819094145c96204e3f0d |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d22871bfe48190909467f07eda0118 |
completed | April 5, 2026, 9:16 a.m. |
| NEDg | Description generation | batch_69d22990ef5881908b6a6100d7dcf6e6 |
completed | April 5, 2026, 9:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d22a0cb0808190a6119dc0268c50b9 |
completed | April 5, 2026, 9:23 a.m. |
Created at: March 30, 2026, 8:32 p.m.