Triple
T1785748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elle |
E39387
|
entity |
| Predicate | hasEdition |
P35
|
FINISHED |
| Object |
Elle Spain
Elle Spain is the Spanish-language edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Spanish readers and culture.
|
E198231
|
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: Elle Spain | Statement: [Elle, hasEdition, Elle Spain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elle Spain Context triple: [Elle, hasEdition, Elle Spain]
-
A.
Alba Flores
Alba Flores is a Spanish actress best known internationally for her role as Nairobi in the television series "Money Heist" (La Casa de Papel).
-
B.
Pilar
Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
-
C.
Pilar
Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
-
D.
Pilar
Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- 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: Elle Spain Triple: [Elle, hasEdition, Elle Spain]
Generated description
Elle Spain is the Spanish-language edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Spanish readers and culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elle Spain Target entity description: Elle Spain is the Spanish-language edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Spanish readers and culture.
-
A.
Alba Flores
Alba Flores is a Spanish actress best known internationally for her role as Nairobi in the television series "Money Heist" (La Casa de Papel).
-
B.
Pilar
Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
-
C.
Pilar
Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
-
D.
Pilar
Pilar is the introspective female protagonist of Paulo Coelho’s novel "By the River Piedra I Sat Down and Wept," whose spiritual and emotional journey drives the story.
-
E.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
- 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_69a88630519c8190a17addd83c4a3ef4 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa650d304481908ad9bff3eadf7da6 |
completed | March 6, 2026, 5:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada9a476448190b072361fe4b41537 |
completed | March 8, 2026, 4:53 p.m. |
| NEDg | Description generation | batch_69adab05cf6c81909f4713664f508ad9 |
completed | March 8, 2026, 4:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adaeb20390819098bad8951ec00d00 |
completed | March 8, 2026, 5:15 p.m. |
Created at: March 4, 2026, 7:31 p.m.