Triple
T10252392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Young Hare |
E240374
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Albertina |
E143258
|
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: Albertina | Statement: [Young Hare, location, Albertina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Albertina Context triple: [Young Hare, location, Albertina]
-
A.
Albertina
chosen
Albertina was the historic University of Königsberg, a prominent Prussian center of learning and research founded in the 16th century.
-
B.
Antoinette
Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
C.
Antoinette
Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
-
D.
Catharina
Catharina of Württemberg was a 19th-century German princess who became Queen consort of Westphalia through her marriage to Jérôme Bonaparte, Napoleon’s youngest brother.
-
E.
Massimiliana
Massimiliana is an Italian feminine given name, serving as the female counterpart of Massimiliano.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23e39988190b75c6421b85d430d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7c6aca08190b15e3790cad12532 |
completed | April 9, 2026, 12:50 a.m. |
Created at: April 6, 2026, 11:29 a.m.