Triple
T19542539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellen Heidingsfelder |
E488941
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Heidingsfelder
Heidingsfelder is a German-origin surname borne by individuals such as American attorney Ellen Heidingsfelder.
|
E1383965
|
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: Heidingsfelder | Statement: [Ellen Heidingsfelder, familyName, Heidingsfelder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heidingsfelder Context triple: [Ellen Heidingsfelder, familyName, Heidingsfelder]
-
A.
Ruhmannsfelden
Ruhmannsfelden is a small market town in the Bavarian Forest region of southeastern Germany.
-
B.
Marktheidenfeld
Marktheidenfeld is a small town in Lower Franconia, Bavaria, Germany, known for its location on the River Main and its historic old town.
-
C.
Heckfeld
Heckfeld is a village and district of the town Lauda-Königshofen in the Main-Tauber region of Baden-Württemberg, Germany.
-
D.
Hakenfelde
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
-
E.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
- 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: Heidingsfelder Triple: [Ellen Heidingsfelder, familyName, Heidingsfelder]
Generated description
Heidingsfelder is a German-origin surname borne by individuals such as American attorney Ellen Heidingsfelder.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Heidingsfelder Target entity description: Heidingsfelder is a German-origin surname borne by individuals such as American attorney Ellen Heidingsfelder.
-
A.
Ruhmannsfelden
Ruhmannsfelden is a small market town in the Bavarian Forest region of southeastern Germany.
-
B.
Marktheidenfeld
Marktheidenfeld is a small town in Lower Franconia, Bavaria, Germany, known for its location on the River Main and its historic old town.
-
C.
Heckfeld
Heckfeld is a village and district of the town Lauda-Königshofen in the Main-Tauber region of Baden-Württemberg, Germany.
-
D.
Hakenfelde
Hakenfelde is a locality in the Berlin borough of Spandau, known for its residential areas, green spaces, and proximity to the Havel River.
-
E.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6387442588190a2cdae60a6a940d6 |
completed | April 20, 2026, 2:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07577880048190bb182c9fe3856254 |
completed | May 15, 2026, 5:27 p.m. |
| NEDg | Description generation | batch_6a07583fc26481908bd2f00c19f3b4cd |
completed | May 15, 2026, 5:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07594a23ac81908807df4605555d3f |
completed | May 15, 2026, 5:35 p.m. |
Created at: April 10, 2026, 1:41 p.m.