Triple
T11786477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of Espírito Santo |
E280282
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Serra |
E946428
|
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: Serra | Statement: [State of Espírito Santo, hasCity, Serra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serra Context triple: [State of Espírito Santo, hasCity, Serra]
-
A.
Serra
Serra is a Spanish surname most famously associated with Junípero Serra, the 18th-century Franciscan friar who founded several missions in what is now California.
-
B.
Serra
chosen
Serra is a major coastal municipality in southeastern Brazil known for its industrial development and role in the Greater Vitória metropolitan area.
-
C.
Rocha
Rocha is a Portuguese-origin surname common in Lusophone countries and among their diasporas.
-
D.
Rocha
Rocha is a coastal department in southeastern Uruguay known for its beaches, lagoons, and ecotourism.
-
E.
Serra Mesa
Serra Mesa is a primarily residential neighborhood in central San Diego, California, known for its suburban character and proximity to major freeways and commercial areas.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a586803481909af0032c35ca6e51 |
completed | April 10, 2026, 7:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f130e5a21881909b59e39cd96ec676 |
completed | April 28, 2026, 10:12 p.m. |
Created at: April 8, 2026, 9:42 p.m.