Triple
T7035210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Maria Laura of Belgium, Archduchess of Austria-Este |
E163364
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Maria Laura
Maria Laura is a Belgian princess and Archduchess of Austria-Este, a member of both the Belgian royal family and the House of Habsburg-Lorraine.
|
E637207
|
NE FINISHED |
How this triple was built (4 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: Maria Laura | Statement: [Princess Maria Laura of Belgium, Archduchess of Austria-Este, givenName, Maria Laura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maria Laura Context triple: [Princess Maria Laura of Belgium, Archduchess of Austria-Este, givenName, Maria Laura]
-
A.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
B.
Maria Paola
Maria Paola is the Italian form of the given name borne by Pauline Bonaparte, Napoleon Bonaparte’s influential and famously beautiful sister.
-
C.
María Mercedes
María Mercedes is a popular 1990s Mexican telenovela starring Thalía as a poor young woman whose life changes dramatically after an unexpected inheritance.
-
D.
Matilde Andrades
Matilde Andrades was the mother of influential American artist Jean-Michel Basquiat.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Maria Laura Triple: [Princess Maria Laura of Belgium, Archduchess of Austria-Este, givenName, Maria Laura]
Generated description
Maria Laura is a Belgian princess and Archduchess of Austria-Este, a member of both the Belgian royal family and the House of Habsburg-Lorraine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maria Laura Target entity description: Maria Laura is a Belgian princess and Archduchess of Austria-Este, a member of both the Belgian royal family and the House of Habsburg-Lorraine.
-
A.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
B.
Maria Paola
Maria Paola is the Italian form of the given name borne by Pauline Bonaparte, Napoleon Bonaparte’s influential and famously beautiful sister.
-
C.
María Mercedes
María Mercedes is a popular 1990s Mexican telenovela starring Thalía as a poor young woman whose life changes dramatically after an unexpected inheritance.
-
D.
Matilde Andrades
Matilde Andrades was the mother of influential American artist Jean-Michel Basquiat.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- F. None of above. chosen
Provenance (5 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e212e28c8190bf38ce9a25d2032e |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c775a211f88190afe5ed466abcac7a |
completed | March 28, 2026, 6:30 a.m. |
| NEDg | Description generation | batch_69c779c064548190bc17a399723f85e7 |
completed | March 28, 2026, 6:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c77a79e76c8190a42fe57ffc1dc23c |
completed | March 28, 2026, 6:51 a.m. |
Created at: March 27, 2026, 2:36 p.m.