Triple
T16109567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gil Samaniego |
E390837
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Samaniego
Samaniego is a surname of Spanish origin borne by various notable individuals, including figures in the arts, sports, and public life.
|
E1197552
|
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: Samaniego | Statement: [Gil Samaniego, hasComponent, Samaniego]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samaniego Context triple: [Gil Samaniego, hasComponent, Samaniego]
-
A.
Montalva
Montalva is a Spanish-language surname notably associated with Chilean president Eduardo Frei Montalva.
-
B.
Larino
Larino is a historic town in the Molise region of southern Italy, known for its Roman amphitheater, medieval architecture, and traditional festivals.
-
C.
Juncal
Juncal is a civil parish in the municipality of Porto de Mós in central Portugal, known for its rural character and local cultural traditions.
-
D.
Moncalvo
Moncalvo is a small historic town in Italy’s Piedmont region, known as one of the country’s smallest cities and for its wine and truffle production.
-
E.
Varela
Varela is a Spanish surname borne by numerous notable figures in politics, the military, arts, and public life across the Spanish-speaking world.
- 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: Samaniego Triple: [Gil Samaniego, hasComponent, Samaniego]
Generated description
Samaniego is a surname of Spanish origin borne by various notable individuals, including figures in the arts, sports, and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Samaniego Target entity description: Samaniego is a surname of Spanish origin borne by various notable individuals, including figures in the arts, sports, and public life.
-
A.
Montalva
Montalva is a Spanish-language surname notably associated with Chilean president Eduardo Frei Montalva.
-
B.
Larino
Larino is a historic town in the Molise region of southern Italy, known for its Roman amphitheater, medieval architecture, and traditional festivals.
-
C.
Juncal
Juncal is a civil parish in the municipality of Porto de Mós in central Portugal, known for its rural character and local cultural traditions.
-
D.
Moncalvo
Moncalvo is a small historic town in Italy’s Piedmont region, known as one of the country’s smallest cities and for its wine and truffle production.
-
E.
Varela
Varela is a Spanish surname borne by numerous notable figures in politics, the military, arts, and public life across the Spanish-speaking world.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2016665c0819081aa7a44b1d08183 |
completed | April 17, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff79c74388190a10e0346426b0cbe |
completed | May 10, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_69fff8de647481908e820b0e14bc7b76 |
completed | May 10, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fff94cd32081908205ae383e58d148 |
completed | May 10, 2026, 3:19 a.m. |
Created at: April 10, 2026, 5 a.m.