Triple
T1336711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pedro |
E28767
|
entity |
| Predicate | shortFormOf |
P43
|
FINISHED |
| Object | San Pedro |
E124932
|
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: San Pedro | Statement: [Pedro, shortFormOf, San Pedro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Pedro Context triple: [Pedro, shortFormOf, San Pedro]
-
A.
San Pedro
chosen
San Pedro is a coastal neighborhood in the city of Los Angeles known for its busy port, waterfront attractions, and maritime heritage.
-
B.
San Pedro
San Pedro is a Chilean commune and town located within the Santiago Metropolitan Region, known for its rural character and agricultural activities.
-
C.
San Pedro Mártir
San Pedro Mártir is a neighborhood within Mexico City’s Tlalpan borough, known for its semi-rural character and location along the southern edge of the metropolis.
-
D.
Rosarito
Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
-
E.
San Fernando
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1edda1c81909a1149b254b0d57e |
completed | March 1, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd47654288190851579e84c36ceac |
completed | March 8, 2026, 1:44 a.m. |
Created at: March 1, 2026, 7:55 p.m.