Triple
T5241675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julio Antonio Mella |
E118356
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Mella |
E115682
|
NE FINISHED |
How this triple was built (2 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: Mella | Statement: [Julio Antonio Mella, familyName, Mella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mella Context triple: [Julio Antonio Mella, familyName, Mella]
-
A.
Mella
chosen
Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
-
B.
Melika
Melika is a historic oasis town in Algeria’s M’zab Valley, known for its traditional Ibadi Muslim community and distinctive Saharan architecture.
-
C.
Meliae
The Meliae are nymphs from Greek mythology associated with ash trees and often linked to the early generations of humanity and rustic woodland life.
-
D.
Mawanella
Mawanella is a town in central Sri Lanka known as a key transit point on the Colombo–Kandy road and for its surrounding rubber and tea plantations.
-
E.
Liluah
Liluah is a suburban locality in the Howrah district of West Bengal, India, known for its residential areas and railway facilities near Kolkata.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd4467db0881909b3b0982df32cc8f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b2c50508190b84bab216c30cbfe |
completed | March 20, 2026, 4:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bef82b42308190b9e3e0e113d8093b |
completed | March 21, 2026, 7:57 p.m. |
Created at: March 20, 2026, 1:49 p.m.