Triple
T16048031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Böblingen |
E389273
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Unterer See
Unterer See is a small lake located in the town of Böblingen in the German state of Baden-Württemberg.
|
E1190854
|
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: Unterer See | Statement: [Böblingen, hasLake, Unterer See]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Unterer See Context triple: [Böblingen, hasLake, Unterer See]
-
A.
Unterseen
Unterseen is a historic Swiss town in the Bernese Oberland, situated near Interlaken at the confluence of the Aare and Lombach rivers with views of the surrounding Alps.
-
B.
Scholl Deep
Scholl Deep is a specific deep-sea depression located within the Mariana Trench, one of the most extreme and least explored oceanic environments on Earth.
-
C.
Obersee
Obersee is a small, picturesque alpine lake in Bavaria, Germany, known for its clear emerald waters and dramatic mountain surroundings near the Königssee.
-
D.
Landsort Deep
Landsort Deep is the deepest point in the Baltic Sea, known for its extreme depth and unique marine environment.
-
E.
Blausee
Blausee is a small, crystal-clear alpine lake in the Swiss Bernese Oberland, famed for its striking blue waters and tranquil forest surroundings.
- 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: Unterer See Triple: [Böblingen, hasLake, Unterer See]
Generated description
Unterer See is a small lake located in the town of Böblingen in the German state of Baden-Württemberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Unterer See Target entity description: Unterer See is a small lake located in the town of Böblingen in the German state of Baden-Württemberg.
-
A.
Unterseen
Unterseen is a historic Swiss town in the Bernese Oberland, situated near Interlaken at the confluence of the Aare and Lombach rivers with views of the surrounding Alps.
-
B.
Scholl Deep
Scholl Deep is a specific deep-sea depression located within the Mariana Trench, one of the most extreme and least explored oceanic environments on Earth.
-
C.
Obersee
Obersee is a small, picturesque alpine lake in Bavaria, Germany, known for its clear emerald waters and dramatic mountain surroundings near the Königssee.
-
D.
Landsort Deep
Landsort Deep is the deepest point in the Baltic Sea, known for its extreme depth and unique marine environment.
-
E.
Blausee
Blausee is a small, crystal-clear alpine lake in the Swiss Bernese Oberland, famed for its striking blue waters and tranquil forest surroundings.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e18360464881909fd4d3bcb4ffb7f5 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbddc25481908fca660c4f14eaff |
completed | May 10, 2026, 1:14 a.m. |
| NEDg | Description generation | batch_69ffdc915be88190a0e949fcee608242 |
completed | May 10, 2026, 1:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffdd17239c8190a3c0c4d146a279f7 |
completed | May 10, 2026, 1:19 a.m. |
Created at: April 10, 2026, 4:56 a.m.