Triple
T8105818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | César Milstein |
E189222
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Bahía Blanca |
E431330
|
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: Bahía Blanca | Statement: [César Milstein, placeOfBirth, Bahía Blanca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bahía Blanca Context triple: [César Milstein, placeOfBirth, Bahía Blanca]
-
A.
Bahía Blanca
chosen
Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
-
B.
Mar del Plata
Mar del Plata is a major Argentine Atlantic coastal city renowned as a popular beach resort and tourist destination.
-
C.
Comodoro Rivadavia
Comodoro Rivadavia is a coastal city in southern Argentina known as a key oil industry hub and one of the main urban centers of Patagonia.
-
D.
Gualeguaychú
Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
-
E.
Colonia Buenos Aires
Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42f735c8819090d0d822644c0a51 |
completed | March 31, 2026, 3:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf5120159881908361bf9cb81e3932 |
completed | April 3, 2026, 5:33 a.m. |
Created at: March 30, 2026, 5:31 p.m.