Triple
T1986999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canino |
E43163
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Arlena di Castro
Arlena di Castro is a small municipality in the province of Viterbo in Italy’s Lazio region, known for its rural landscape and proximity to Lake Bolsena.
|
E234999
|
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: Arlena di Castro | Statement: [Canino, locatedNear, Arlena di Castro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arlena di Castro Context triple: [Canino, locatedNear, Arlena di Castro]
-
A.
Celia María Cuccittini
Celia María Cuccittini is an Argentine woman best known as the mother of football legend Lionel Messi.
-
B.
Adriana Caselotti
Adriana Caselotti was an American actress and singer best known for providing the original voice of Snow White in Disney’s pioneering 1937 animated feature.
-
C.
Matilde Andrades
Matilde Andrades was the mother of influential American artist Jean-Michel Basquiat.
-
D.
Lilia Vetti
Lilia Vetti was the wife of famed French singer and actor Tino Rossi.
-
E.
Barbara Luna
Barbara Luna is an American actress known for her numerous film and television roles from the 1950s onward, including appearances in productions such as the historical drama "Che!" and the original "Star Trek" series.
- 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: Arlena di Castro Triple: [Canino, locatedNear, Arlena di Castro]
Generated description
Arlena di Castro is a small municipality in the province of Viterbo in Italy’s Lazio region, known for its rural landscape and proximity to Lake Bolsena.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arlena di Castro Target entity description: Arlena di Castro is a small municipality in the province of Viterbo in Italy’s Lazio region, known for its rural landscape and proximity to Lake Bolsena.
-
A.
Celia María Cuccittini
Celia María Cuccittini is an Argentine woman best known as the mother of football legend Lionel Messi.
-
B.
Adriana Caselotti
Adriana Caselotti was an American actress and singer best known for providing the original voice of Snow White in Disney’s pioneering 1937 animated feature.
-
C.
Matilde Andrades
Matilde Andrades was the mother of influential American artist Jean-Michel Basquiat.
-
D.
Lilia Vetti
Lilia Vetti was the wife of famed French singer and actor Tino Rossi.
-
E.
Barbara Luna
Barbara Luna is an American actress known for her numerous film and television roles from the 1950s onward, including appearances in productions such as the historical drama "Che!" and the original "Star Trek" series.
- 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_69a88713ddc88190a969715658ebe7a8 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb840a5708190a9b64564b855fb22 |
completed | March 7, 2026, 5:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae30467f2c8190adc3e619396c0f08 |
completed | March 9, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69ae31722f0081908a4d9d0760af375e |
completed | March 9, 2026, 2:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae3209e46c81909055a1ee4fccd74d |
completed | March 9, 2026, 2:35 a.m. |
Created at: March 4, 2026, 7:37 p.m.