Triple
T1687019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galicia |
E36464
|
entity |
| Predicate | containsProvince |
P11085
|
FINISHED |
| Object | Lugo |
E191706
|
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: Lugo | Statement: [Galicia, containsProvince, Lugo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lugo Context triple: [Galicia, containsProvince, Lugo]
-
A.
Lugo
chosen
Lugo is a historic city in northwestern Spain known for its remarkably well-preserved Roman walls, a UNESCO World Heritage Site.
-
B.
Gavignano
Gavignano is a small Italian town in the Lazio region, historically notable as the birthplace of Pope Innocent III.
-
C.
Logudoro
Logudoro is a historical-cultural region in northern Sardinia known for its distinctive Sardinian dialect, medieval heritage, and rural landscapes.
-
D.
Neiva
Neiva is a major city in southwestern Colombia known as the economic and cultural center of the upper Magdalena River valley.
-
E.
Rosario
Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
- 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_69a886151508819084fa7f1ce6e05577 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa6293c368819094ab0f615e418647 |
completed | March 6, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8ac462f0819094e7a5751c6975c6 |
completed | March 8, 2026, 2:42 p.m. |
Created at: March 4, 2026, 7:29 p.m.