Triple
T4588833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Castle |
E103433
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Frieda |
E415848
|
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: Frieda | Statement: [The Castle, hasCharacter, Frieda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frieda Context triple: [The Castle, hasCharacter, Frieda]
-
A.
Freda
chosen
Freda is a feminine given name, often used in English-speaking countries and derived from names like Winifred or Frederica.
-
B.
Berta
Berta is a Nilo-Saharan language spoken primarily in parts of Sudan and Ethiopia.
-
C.
Berta
Berta is a fictional character in Paulo Coelho’s novel "The Devil and Miss Prym," serving as one of the villagers whose life and choices reflect the book’s central moral and spiritual dilemmas.
-
D.
Baerbel
Baerbel is a feminine given name of German origin, commonly used as an alternative spelling of Bärbel.
-
E.
Franziska
Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
- 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_69bd43dccaf08190aa89e9991a289719 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd592115fc8190b1aee1d8bbaf1ee3 |
completed | March 20, 2026, 2:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bde0b9f700819082b0e5171132d0f3 |
completed | March 21, 2026, 12:05 a.m. |
Created at: March 20, 2026, 1:11 p.m.