Triple
T15329400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Luis Province |
E366492
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Villa Mercedes |
E887981
|
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: Villa Mercedes | Statement: [San Luis Province, contains, Villa Mercedes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Villa Mercedes Context triple: [San Luis Province, contains, Villa Mercedes]
-
A.
Villa Mercedes
chosen
Villa Mercedes is a major urban and industrial center in central Argentina, located in the province of San Luis.
-
B.
Villa Victoria
Villa Victoria is a municipality in the State of Mexico known for its rural landscapes, reservoirs, and agricultural communities west of Mexico City.
-
C.
Villa Las Estrellas
Villa Las Estrellas is a small Chilean civilian settlement and research support community located on King George Island in Antarctica.
-
D.
Villa Alegre
Villa Alegre is a small rural municipality in Chile’s Maule Region, known for its agricultural activity and traditional central valley landscapes.
-
E.
Villa Gesell
Villa Gesell is a popular seaside resort city on Argentina’s Atlantic coast, known for its wide sandy beaches, dunes, and vibrant summer tourism.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e0161ac8190aa1d52c063c02ad0 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8b1b2d08190a158bf65535ad750 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.