Triple
T4472687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cañete |
E98530
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Mala |
E440056
|
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: Mala | Statement: [Cañete, hasMajorCity, Mala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mala Context triple: [Cañete, hasMajorCity, Mala]
-
A.
Mala
chosen
Mala is a coastal town in Peru’s Lima Region known for its agricultural production and beaches along the Pacific Ocean.
-
B.
Maluma
Maluma is a Colombian reggaeton and Latin pop singer-songwriter known for his chart-topping hits and major influence in contemporary Latin music.
-
C.
Malaipadukadām
Malaipadukadām is a classical Tamil poem from the Sangam era renowned for its vivid portrayal of mountainous landscapes, tribal life, and romantic love.
-
D.
Dimalik
Dimalik is the indigenous traditional religion of the Dimasa people, encompassing their ancestral deities, rituals, and cosmological beliefs.
-
E.
Mella
Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
- 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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356b95c888190a84bf4a9b2c60aa6 |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b628764bf081909a7a1079d0176c66 |
completed | March 15, 2026, 3:33 a.m. |
Created at: March 12, 2026, 11:35 p.m.