Triple
T3295770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalwaria Zebrzydowska |
E69210
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Fátima |
E105859
|
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: Fátima | Statement: [Kalwaria Zebrzydowska, twinTown, Fátima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fátima Context triple: [Kalwaria Zebrzydowska, twinTown, Fátima]
-
A.
Fátima
chosen
Fátima is a town in central Portugal renowned as a major Catholic pilgrimage site associated with reported Marian apparitions in 1917.
-
B.
María
María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
-
C.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
D.
Amparo
Amparo is a municipality in the interior of Brazil known for its historical architecture and role in the coffee-producing region of the state of São Paulo.
-
E.
Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
- 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_69ad859e529c8190a404273f53cb487d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb077c60c81909782be5202ce5a43 |
completed | March 8, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e86e867c8190a1e5042a4d4d92cb |
completed | March 12, 2026, 4:23 p.m. |
Created at: March 8, 2026, 3:10 p.m.