Triple
T20722262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Ingeborg of Denmark |
E509345
|
entity |
| Predicate | nobleFamily |
P914
|
FINISHED |
| Object | Glücksburg |
—
|
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: Glücksburg | Statement: [Princess Ingeborg of Denmark, nobleFamily, Glücksburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glücksburg Context triple: [Princess Ingeborg of Denmark, nobleFamily, Glücksburg]
-
A.
Glücksburg
chosen
Glücksburg is a small town in northern Germany known as the ancestral seat of the House of Schleswig-Holstein-Sonderburg-Glücksburg, a prominent European royal dynasty.
-
B.
Glücksburg
Glücksburg is a European royal house of German origin that has provided monarchs to several countries, including Denmark, Norway, and Greece.
-
C.
Lauenburg
Lauenburg is a historic town in northern Germany situated on the banks of the Elbe River.
-
D.
Borghorst
Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
-
E.
Himmerich
Himmerich is a hill in Germany’s Siebengebirge range, known for its forested slopes and hiking trails overlooking the Rhine valley.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d6bdcc8190ba42c44159a2c0d5 |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:27 p.m.