Triple
T12052858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pere Milà |
E286960
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Roser Segimon
Roser Segimon was a wealthy Catalan heiress and patron whose marriage to industrialist Pere Milà helped finance and inspire Antoni Gaudí’s iconic Casa Milà (La Pedrera) in Barcelona.
|
E286961
|
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: Roser Segimon | Statement: [Pere Milà, spouse, Roser Segimon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roser Segimon Context triple: [Pere Milà, spouse, Roser Segimon]
-
A.
Rosario Segimon
Rosario Segimon was a wealthy Catalan patron who financed the construction of Antoni Gaudí’s iconic Casa Milà in Barcelona.
-
B.
Rine-chan
Rine-chan is a female cheerleader-style mascot character for the Japanese professional baseball team Chiba Lotte Marines.
-
C.
Mamoru
Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
-
D.
Totsuko
Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
-
E.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
- 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: Roser Segimon Triple: [Pere Milà, spouse, Roser Segimon]
Generated description
Roser Segimon was a wealthy Catalan heiress and patron whose marriage to industrialist Pere Milà helped finance and inspire Antoni Gaudí’s iconic Casa Milà (La Pedrera) in Barcelona.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Roser Segimon Target entity description: Roser Segimon was a wealthy Catalan heiress and patron whose marriage to industrialist Pere Milà helped finance and inspire Antoni Gaudí’s iconic Casa Milà (La Pedrera) in Barcelona.
-
A.
Rosario Segimon
chosen
Rosario Segimon was a wealthy Catalan patron who financed the construction of Antoni Gaudí’s iconic Casa Milà in Barcelona.
-
B.
Rine-chan
Rine-chan is a female cheerleader-style mascot character for the Japanese professional baseball team Chiba Lotte Marines.
-
C.
Mamoru
Mamoru is a Japanese masculine given name commonly borne by notable figures in politics, arts, and entertainment.
-
D.
Totsuko
Totsuko is the former abbreviated name of Tokyo Tsushin Kogyo, the Japanese company that later became Sony.
-
E.
Tsutako
Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
- F. None of above.
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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90423b22081908fba82fbc6b40eb5 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49ddde6548190adae2a889ec5c72b |
completed | May 1, 2026, 12:34 p.m. |
| NEDg | Description generation | batch_69f53d95d4fc8190b5f4e460646bec2a |
completed | May 1, 2026, 11:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f564d2b4348190abf2d09ae00aea37 |
completed | May 2, 2026, 2:43 a.m. |
Created at: April 8, 2026, 9:47 p.m.