Triple
T1654761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saalekreis |
E35772
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Leuna
Leuna is a town in the Saalekreis district of Saxony-Anhalt, Germany, historically known for its large chemical industry complex.
|
E186997
|
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: Leuna | Statement: [Saalekreis, contains, Leuna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leuna Context triple: [Saalekreis, contains, Leuna]
-
A.
Risca
Risca is a town in south Wales situated in the county borough of Caerphilly, near Newport, with a history rooted in coal mining and industry.
-
B.
Clausthal
Clausthal is a historic mining town in Lower Saxony, Germany, best known today for its technical university and association with figures like microbiologist Robert Koch.
-
C.
Dillenburg
Dillenburg is a historic town in the German state of Hesse, known as the ancestral seat of the House of Orange-Nassau and its connection to Dutch history.
-
D.
Salzgitter
Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
-
E.
Haldia
Haldia is an industrial port city in eastern India known for its petrochemical complexes and role as a major river port on the Hooghly River.
- 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: Leuna Triple: [Saalekreis, contains, Leuna]
Generated description
Leuna is a town in the Saalekreis district of Saxony-Anhalt, Germany, historically known for its large chemical industry complex.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leuna Target entity description: Leuna is a town in the Saalekreis district of Saxony-Anhalt, Germany, historically known for its large chemical industry complex.
-
A.
Risca
Risca is a town in south Wales situated in the county borough of Caerphilly, near Newport, with a history rooted in coal mining and industry.
-
B.
Clausthal
Clausthal is a historic mining town in Lower Saxony, Germany, best known today for its technical university and association with figures like microbiologist Robert Koch.
-
C.
Dillenburg
Dillenburg is a historic town in the German state of Hesse, known as the ancestral seat of the House of Orange-Nassau and its connection to Dutch history.
-
D.
Salzgitter
Salzgitter is a major industrial city in central Germany known for its steel production and location within the federal state of Lower Saxony.
-
E.
Haldia
Haldia is an industrial port city in eastern India known for its petrochemical complexes and role as a major river port on the Hooghly River.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a8b597c81908a62b41718d85df6 |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60ad3bb48190a096ff7813748168 |
completed | March 8, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_69ad61ff65b881909009c230780a146e |
completed | March 8, 2026, 11:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad62ec3a80819085fef1c378b9abdc |
completed | March 8, 2026, 11:52 a.m. |
Created at: March 4, 2026, 7:29 p.m.