Triple
T20966117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Vicente (Lisbon) |
E516370
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Alfama |
—
|
NE NERFINISHED |
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: Alfama | Statement: [São Vicente (Lisbon), contains, Alfama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alfama Context triple: [São Vicente (Lisbon), contains, Alfama]
-
A.
Alfama
chosen
Alfama is Lisbon’s oldest and most traditional neighborhood, known for its steep, narrow streets, historic architecture, and vibrant fado music culture.
-
B.
Tangier medina
Tangier medina is the historic old quarter of Tangier, Morocco, characterized by its narrow winding streets, traditional markets, and blend of Moorish, Andalusian, and European influences.
-
C.
Sintra
Sintra is a historic Portuguese town near Lisbon, renowned for its romantic 19th-century palaces, castles, and lush hillside landscapes.
-
D.
Albaicín
Albaicín is Granada’s historic Moorish quarter, known for its narrow winding streets, whitewashed houses, and panoramic views of the Alhambra.
-
E.
Tetuán
Tetuán is a station on the Madrid Metro network serving the Tetuán district in the north of Spain’s capital.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4fde6c48190af1398e7e734629e |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fb74206c819099f9c67eaca45ec1 |
completed | April 21, 2026, 4:22 a.m. |
Created at: April 16, 2026, 1:33 p.m.