Triple
T14379016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gjertrud Schnackenberg |
E356551
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Gjertrud
Gjertrud is a feminine given name of Germanic origin, most notably borne by the American poet Gjertrud Schnackenberg.
|
E1096032
|
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: Gjertrud | Statement: [Gjertrud Schnackenberg, givenName, Gjertrud]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gjertrud Context triple: [Gjertrud Schnackenberg, givenName, Gjertrud]
-
A.
Gerda
Gerda is the brave and devoted young heroine of Hans Christian Andersen’s fairy tale who embarks on a perilous journey to rescue her friend Kai from the Snow Queen.
-
B.
Birgitte
Birgitte is a Danish-born member of the British royal family who holds the title Duchess of Gloucester.
-
C.
Grete
Grete is the given name of Grete Hermann, a German mathematician and philosopher known for her pioneering work in the foundations of quantum mechanics and computer algebra.
-
D.
Gitte
Gitte is a feminine given name commonly used in Scandinavian countries, particularly Denmark.
-
E.
Elfriede
Elfriede is a feminine given name of German origin, notably borne by Austrian Nobel Prize–winning writer Elfriede Jelinek.
- 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: Gjertrud Triple: [Gjertrud Schnackenberg, givenName, Gjertrud]
Generated description
Gjertrud is a feminine given name of Germanic origin, most notably borne by the American poet Gjertrud Schnackenberg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gjertrud Target entity description: Gjertrud is a feminine given name of Germanic origin, most notably borne by the American poet Gjertrud Schnackenberg.
-
A.
Gerda
Gerda is the brave and devoted young heroine of Hans Christian Andersen’s fairy tale who embarks on a perilous journey to rescue her friend Kai from the Snow Queen.
-
B.
Birgitte
Birgitte is a Danish-born member of the British royal family who holds the title Duchess of Gloucester.
-
C.
Grete
Grete is the given name of Grete Hermann, a German mathematician and philosopher known for her pioneering work in the foundations of quantum mechanics and computer algebra.
-
D.
Gitte
Gitte is a feminine given name commonly used in Scandinavian countries, particularly Denmark.
-
E.
Elfriede
Elfriede is a feminine given name of German origin, notably borne by Austrian Nobel Prize–winning writer Elfriede Jelinek.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900a67e08190ab1dcf36e6bb3405 |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c5728fc819089ef3c7c34b10101 |
completed | May 8, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fd4e4bae188190a8d1c5b833d58cd8 |
completed | May 8, 2026, 2:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd4f5782b4819081d32dbef032ac61 |
completed | May 8, 2026, 2:49 a.m. |
Created at: April 10, 2026, 1:16 a.m.