Triple
T20916652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helena |
E515089
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Dona Úrsula
Dona Úrsula is a fictional character who serves as the central figure in the story featuring Helena as its protagonist.
|
E1457641
|
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: Dona Úrsula | Statement: [Helena, mainCharacter, Dona Úrsula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dona Úrsula Context triple: [Helena, mainCharacter, Dona Úrsula]
-
A.
Santa Úrsula
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
-
B.
Santa Úrsula
Santa Úrsula is a coastal municipality on the northern side of Tenerife in Spain’s Canary Islands, known for its steep Atlantic cliffs, vineyards, and residential tourism.
-
C.
Uršula
Uršula is a given name, commonly used in various European countries as a variant of the name Ursula.
-
D.
Dona Paula
Dona Paula is a popular coastal tourist destination near Panaji in Goa, India, known for its scenic sea views, romantic legends, and water sports.
-
E.
Raimunda
Raimunda is the resilient and resourceful working-class mother portrayed by Penélope Cruz in Pedro Almodóvar’s film "Volver," around whom the story’s family drama and secrets revolve.
- 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: Dona Úrsula Triple: [Helena, mainCharacter, Dona Úrsula]
Generated description
Dona Úrsula is a fictional character who serves as the central figure in the story featuring Helena as its protagonist.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dona Úrsula Target entity description: Dona Úrsula is a fictional character who serves as the central figure in the story featuring Helena as its protagonist.
-
A.
Santa Úrsula
Santa Úrsula is a neighborhood in Mexico City best known for hosting the iconic Estadio Azteca football stadium.
-
B.
Santa Úrsula
Santa Úrsula is a coastal municipality on the northern side of Tenerife in Spain’s Canary Islands, known for its steep Atlantic cliffs, vineyards, and residential tourism.
-
C.
Uršula
Uršula is a given name, commonly used in various European countries as a variant of the name Ursula.
-
D.
Dona Paula
Dona Paula is a popular coastal tourist destination near Panaji in Goa, India, known for its scenic sea views, romantic legends, and water sports.
-
E.
Raimunda
Raimunda is the resilient and resourceful working-class mother portrayed by Penélope Cruz in Pedro Almodóvar’s film "Volver," around whom the story’s family drama and secrets revolve.
- 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec635f4881909a560fb891100d8c |
completed | April 21, 2026, 3:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a091fc4d81081908c412567cd0e5073 |
completed | May 17, 2026, 1:54 a.m. |
| NEDg | Description generation | batch_6a09210bde4c8190841af0f9a29ecdd9 |
completed | May 17, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09217fdcd08190bc41700ec09851e1 |
completed | May 17, 2026, 2:01 a.m. |
Created at: April 16, 2026, 12:48 p.m.