Triple
T10497458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gave de Pau |
E247575
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Lourdes |
E418856
|
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: Lourdes | Statement: [Gave de Pau, flowsThrough, Lourdes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lourdes Context triple: [Gave de Pau, flowsThrough, Lourdes]
-
A.
Lourdes
Lourdes is an 1894 novel by Émile Zola that critically explores religious faith, pilgrimage, and alleged miracles surrounding the famous Marian shrine in southwestern France.
-
B.
Lourdes
chosen
Lourdes is a town in southwestern France renowned as a major Catholic pilgrimage site associated with Marian apparitions and reputed healing waters.
-
C.
Lourdes
Lourdes is a member of the 2nd Massachusetts Militia Regiment, a historic military unit associated with the U.S. state of Massachusetts.
-
D.
Lourdes
Lourdes is a teenage witch character in the 2020 supernatural horror film "The Craft: Legacy," serving as one of the members of the new coven.
-
E.
Nossa Senhora de Lourdes
Nossa Senhora de Lourdes is a small Brazilian municipality in the state of Sergipe, known for its rural character and location in the semi-arid interior region.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5098d8ac481909c4adedbc4ad1c03 |
completed | April 7, 2026, 1:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcc5816c8190a6a3927797942266 |
completed | April 10, 2026, 11:19 a.m. |
Created at: April 6, 2026, 12:25 p.m.