Triple
T23421028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adolph Tidemand |
E560650
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Clara Charlotte von Schmid |
—
|
NE NERFINISHED |
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: Clara Charlotte von Schmid | Statement: [Adolph Tidemand, spouse, Clara Charlotte von Schmid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clara Charlotte von Schmid Context triple: [Adolph Tidemand, spouse, Clara Charlotte von Schmid]
-
A.
Clara Charlotte von Schmid
chosen
Clara Charlotte von Schmid was the wife of Norwegian romantic nationalist painter Adolph Tidemand.
-
B.
Clara Lauckner
Clara Lauckner was the wife of prominent German dramatist and novelist Hermann Sudermann, associated with his personal and literary life in late 19th- and early 20th-century Germany.
-
C.
Clara Beyer
Clara Beyer is known as the daughter of American politician and diplomat Don Beyer.
-
D.
Clara Sesemann
Clara Sesemann is a kind, wheelchair-bound girl from Frankfurt who becomes Heidi’s close friend in Johanna Spyri’s classic novel "Heidi."
-
E.
Catharina Wilhelmina Schweickhardt
Catharina Wilhelmina Schweickhardt was a Dutch woman best known as the wife of prominent poet and historian Willem Bilderdijk.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454cb1108190ab21ada5411a7146 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a54605dc81909aad9834ef6ff8a1 |
completed | April 29, 2026, 6:29 a.m. |
Created at: April 17, 2026, 5:45 p.m.