Triple
T23070392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mariona Caldentey |
E575177
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Caldentey
Caldentey is a Spanish surname most notably borne by professional footballer Mariona Caldentey, a forward for FC Barcelona and the Spain national team.
|
E1569873
|
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: Caldentey | Statement: [Mariona Caldentey, familyName, Caldentey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caldentey Context triple: [Mariona Caldentey, familyName, Caldentey]
-
A.
Lauterbourg
Lauterbourg is a small French town in the Alsace region near the German border, known for its cross-border role within the Upper Rhine area and its historic Rhine river setting.
-
B.
Hauenstein
Hauenstein is a municipality in the canton of Solothurn in Switzerland, known for its rural setting in the Jura Mountains region.
-
C.
Trichardt
Trichardt is a small town in Mpumalanga, South Africa, known for its close association with the nearby industrial and coal-mining hub of Secunda.
-
D.
Schenevus
Schenevus is a small village in upstate New York known for its rural character and historic charm.
-
E.
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
- 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: Caldentey Triple: [Mariona Caldentey, familyName, Caldentey]
Generated description
Caldentey is a Spanish surname most notably borne by professional footballer Mariona Caldentey, a forward for FC Barcelona and the Spain national team.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caldentey Target entity description: Caldentey is a Spanish surname most notably borne by professional footballer Mariona Caldentey, a forward for FC Barcelona and the Spain national team.
-
A.
Lauterbourg
Lauterbourg is a small French town in the Alsace region near the German border, known for its cross-border role within the Upper Rhine area and its historic Rhine river setting.
-
B.
Hauenstein
Hauenstein is a municipality in the canton of Solothurn in Switzerland, known for its rural setting in the Jura Mountains region.
-
C.
Trichardt
Trichardt is a small town in Mpumalanga, South Africa, known for its close association with the nearby industrial and coal-mining hub of Secunda.
-
D.
Schenevus
Schenevus is a small village in upstate New York known for its rural character and historic charm.
-
E.
Hagenborgh
Hagenborgh is a notable landmark building in the Dutch city of Almelo, recognized for its prominent role in the local urban landscape.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c5f17348190ab92cfdae9bcaeba |
completed | April 29, 2026, 4:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c15a766f081909f8bef872b07a3c0 |
completed | May 19, 2026, 7:47 a.m. |
| NEDg | Description generation | batch_6a0c182a6f4c81909207690b4d241493 |
completed | May 19, 2026, 7:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c18da526c8190b54f9a440f7471e1 |
completed | May 19, 2026, 8:01 a.m. |
Created at: April 17, 2026, 3:56 p.m.