Triple
T11724923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pilar Roldán |
E278738
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Pilar
Pilar is a feminine given name of Spanish origin commonly used in Spanish-speaking countries.
|
E580454
|
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: Pilar | Statement: [Pilar Roldán, givenName, Pilar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pilar Context triple: [Pilar Roldán, givenName, Pilar]
-
A.
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."
-
B.
Pilar
Pilar is a riverside city in southwestern Paraguay known for its colonial architecture, river port activities, and proximity to the border with Argentina.
-
C.
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.
-
D.
Pilar
Pilar is a coastal town on Siargao Island in the Philippines, known for its fishing communities and access to popular surfing and eco-tourism spots.
-
E.
Pilar
Pilar is a city in the Buenos Aires Province of Argentina, known as a growing residential and commercial hub within the Greater Buenos Aires metropolitan area.
- 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: Pilar Triple: [Pilar Roldán, givenName, Pilar]
Generated description
Pilar is a feminine given name of Spanish origin commonly used in Spanish-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pilar Target entity description: Pilar is a feminine given name of Spanish origin commonly used in Spanish-speaking countries.
-
A.
Pilar
chosen
Pilar is a Spanish feminine given name, often associated with religious devotion to Our Lady of the Pillar and traditionally used in Spain and Spanish-speaking countries.
-
B.
Pilar
Pilar is a city in the Buenos Aires Province of Argentina, known as a growing residential and commercial hub within the Greater Buenos Aires metropolitan area.
-
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 a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
-
E.
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.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4d603cc8190b2e68d0bdd793362 |
completed | April 10, 2026, 7:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef83d9fe70819089b9f3585188f96c |
completed | April 27, 2026, 3:42 p.m. |
| NEDg | Description generation | batch_69ef96b13be881908102ffa867f96c22 |
completed | April 27, 2026, 5:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69efb51113708190998b570c33b9d0e7 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 8, 2026, 9:41 p.m.