Triple
T4722459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paraná Delta |
E104799
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Rosario |
E99633
|
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: Rosario | Statement: [Paraná Delta, nearCity, Rosario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosario Context triple: [Paraná Delta, nearCity, Rosario]
-
A.
Rosario
chosen
Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
-
B.
Rosario
Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
-
C.
Rosario
Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
-
D.
Rosario
Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
-
E.
El Rosario
El Rosario is a municipality on the island of Tenerife in Spain’s Canary Islands, known for its coastal landscapes and proximity to the island’s capital, Santa Cruz de Tenerife.
- 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_69bd43ed84648190ae0b7ee8e8d00482 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd642b82008190bb70dd60a4149502 |
completed | March 20, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be1094fd9c81909e12723394f9ae0a |
completed | March 21, 2026, 3:29 a.m. |
Created at: March 20, 2026, 1:18 p.m.