Triple
T8181334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SUBE card |
E191067
|
entity |
| Predicate | usedIn |
P98
|
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: [SUBE card, usedIn, Rosario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosario Context triple: [SUBE card, usedIn, 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 province of Northern Samar in the Eastern Visayas region of the Philippines.
-
C.
Rosario
Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
-
D.
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.
-
E.
Rosario
Rosario is a first-class agricultural municipality in the province of Batangas in the Philippines, known for its coconut and rice farming.
- 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_69ca82c4538081909404325aa5639483 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4c4c2e388190b86854f8b1765e61 |
completed | March 31, 2026, 4:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc68ea260819094ae0f87abdd8041 |
completed | April 2, 2026, 1:29 a.m. |
Created at: March 30, 2026, 5:40 p.m.