Triple
T111218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guillermo Navarro |
E2251
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Navarro
Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
|
E18129
|
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: Navarro | Statement: [Guillermo Navarro, familyName, Navarro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navarro Context triple: [Guillermo Navarro, familyName, Navarro]
-
A.
San Carlos
San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
-
B.
Ovalle
Ovalle is a Chilean city known as an agricultural and commercial center in the north-central part of the country.
-
C.
Ciudad Serdán
Ciudad Serdán is a town in the Mexican state of Puebla, known as a gateway community to the nearby Pico de Orizaba volcano and surrounding highland region.
-
D.
Pinales
Pinales is the botanical order of coniferous trees and shrubs that includes pines, firs, spruces, and related needle-leaved, cone-bearing plants.
-
E.
Milpa Alta
Milpa Alta is a largely rural borough in the southern part of Mexico City, known for its traditional agriculture, especially nopal cactus cultivation, and strong preservation of indigenous customs.
- 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: Navarro Triple: [Guillermo Navarro, familyName, Navarro]
Generated description
Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Navarro Target entity description: Navarro is a Spanish surname borne by numerous notable individuals across fields such as film, sports, politics, and academia.
-
A.
San Carlos
San Carlos is a city in San Mateo County, California, located on the San Francisco Peninsula between Belmont and Redwood City.
-
B.
Ovalle
Ovalle is a Chilean city known as an agricultural and commercial center in the north-central part of the country.
-
C.
Ciudad Serdán
Ciudad Serdán is a town in the Mexican state of Puebla, known as a gateway community to the nearby Pico de Orizaba volcano and surrounding highland region.
-
D.
Pinales
Pinales is the botanical order of coniferous trees and shrubs that includes pines, firs, spruces, and related needle-leaved, cone-bearing plants.
-
E.
Milpa Alta
Milpa Alta is a largely rural borough in the southern part of Mexico City, known for its traditional agriculture, especially nopal cactus cultivation, and strong preservation of indigenous customs.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a256ec650c8190bee2067e37065527 |
completed | Feb. 28, 2026, 2:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2c52b004c8190a74c495d3213973d |
completed | Feb. 28, 2026, 10:36 a.m. |
| NEDg | Description generation | batch_69a2c64913888190a09afdf3c8f3ab46 |
completed | Feb. 28, 2026, 10:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2c717e9b88190a44b26128970a286 |
completed | Feb. 28, 2026, 10:44 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.