Triple
T3180947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ingrid Mössinger |
E66583
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Ingrid |
E108797
|
NE FINISHED |
How this triple was built (2 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: Ingrid | Statement: [Ingrid Mössinger, hasGivenName, Ingrid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ingrid Context triple: [Ingrid Mössinger, hasGivenName, Ingrid]
-
A.
Ingrid
chosen
Ingrid is a feminine given name of Scandinavian origin that has been borne by several notable figures, including the Swedish actress Ingrid Bergman.
-
B.
Ingeborg
Ingeborg is a feminine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
-
C.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
-
D.
Astrid
Astrid is a Belgian princess and member of the country’s royal family.
-
E.
Karin
Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69b235fd1688819080fa1d73d2b233ba |
completed | March 12, 2026, 3:41 a.m. |
Created at: March 8, 2026, 3:06 p.m.