Triple
T17045102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santarém District |
E413546
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Azambuja |
E374178
|
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: Azambuja | Statement: [Santarém District, hasMunicipality, Azambuja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Azambuja Context triple: [Santarém District, hasMunicipality, Azambuja]
-
A.
Azambuja
chosen
Azambuja is a municipality in Portugal known for its agricultural landscape and proximity to the Lisbon metropolitan area.
-
B.
Luso
Luso is a Portuguese civil parish in the municipality of Mealhada, known for its mineral water springs and proximity to the Bussaco Forest.
-
C.
Luso
Luso is the former colonial-era name of the city now known as Luena, the capital of Moxico Province in eastern Angola.
-
D.
Itumbiara
Itumbiara is a municipality in the Brazilian state of Goiás, known for its strategic location on the Paranaíba River and its role as a regional economic and transportation hub.
-
E.
Fajão
Fajão is a small village in central Portugal, situated in the mountainous region of the Arganil municipality.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3da9d7e988190a5e3991c7123f9b0 |
completed | April 18, 2026, 7:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01233cd3d48190b002951881ef670b |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 10, 2026, 5:33 a.m.