Triple
T3180948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ingrid Mössinger |
E66583
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Mössinger
Mössinger is a German surname most notably borne by Ingrid Mössinger, a prominent figure in the German art and museum world.
|
E351219
|
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: Mössinger | Statement: [Ingrid Mössinger, hasFamilyName, Mössinger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mössinger Context triple: [Ingrid Mössinger, hasFamilyName, Mössinger]
-
A.
Löhr
Löhr is a German-language surname borne by various notable individuals, including figures in military, arts, and public life.
-
B.
Böbing
Böbing is a small municipality in the Weilheim-Schongau district of Bavaria, Germany, known for its rural setting in the Alpine foothills.
-
C.
Kölzig
Kölzig is the surname of former professional ice hockey goaltender Olie Kolzig, best known for his long NHL career with the Washington Capitals.
-
D.
Hohneck
Hohneck is one of the highest peaks in the Vosges Mountains of northeastern France, known for its panoramic views and popular hiking and skiing opportunities.
-
E.
Tureberg
Tureberg is a central district in Sollentuna Municipality, Sweden, known for housing the municipal center and key public services.
- 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: Mössinger Triple: [Ingrid Mössinger, hasFamilyName, Mössinger]
Generated description
Mössinger is a German surname most notably borne by Ingrid Mössinger, a prominent figure in the German art and museum world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mössinger Target entity description: Mössinger is a German surname most notably borne by Ingrid Mössinger, a prominent figure in the German art and museum world.
-
A.
Löhr
Löhr is a German-language surname borne by various notable individuals, including figures in military, arts, and public life.
-
B.
Böbing
Böbing is a small municipality in the Weilheim-Schongau district of Bavaria, Germany, known for its rural setting in the Alpine foothills.
-
C.
Kölzig
Kölzig is the surname of former professional ice hockey goaltender Olie Kolzig, best known for his long NHL career with the Washington Capitals.
-
D.
Hohneck
Hohneck is one of the highest peaks in the Vosges Mountains of northeastern France, known for its panoramic views and popular hiking and skiing opportunities.
-
E.
Tureberg
Tureberg is a central district in Sollentuna Municipality, Sweden, known for housing the municipal center and key public services.
- 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_69ad8586a34c8190944c63ec11a8de1a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6a1280c8190b59a2afd30312c02 |
completed | March 8, 2026, 4:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b324ea81f4819080e836dccf8254d0 |
completed | March 12, 2026, 8:41 p.m. |
| NEDg | Description generation | batch_69b326960de48190abe69b3c140f4a4a |
completed | March 12, 2026, 8:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b327b13c9c8190b8c431ca2ae61ef9 |
completed | March 12, 2026, 8:53 p.m. |
Created at: March 8, 2026, 3:06 p.m.