Triple
T5486844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ribatejo |
E123602
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tomar |
E371690
|
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: Tomar | Statement: [Ribatejo, hasCity, Tomar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomar Context triple: [Ribatejo, hasCity, Tomar]
-
A.
Tomar
chosen
Tomar is a historic Portuguese city in the Santarém District, best known for its Templar-founded Convent of Christ, a UNESCO World Heritage site.
-
B.
Valdemoro
Valdemoro is a municipality and growing suburban town in central Spain, located south of Madrid.
-
C.
Olmedo
Olmedo is a small town in the Gallura region of northern Sardinia, Italy, known for its rural character and traditional Sardinian culture.
-
D.
Majadahonda
Majadahonda is a suburban municipality west of Madrid, Spain, known for its residential character, shopping centers, and sports facilities.
-
E.
Andújar
Andújar is a historic town in the province of Jaén, Andalusia, Spain, known for its olive oil production and its location near the Sierra de Andújar Natural Park.
- 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_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd92639b3481908845c280d334117f |
completed | March 20, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf48aa12708190add69c5fd51d161d |
completed | March 22, 2026, 1:40 a.m. |
Created at: March 20, 2026, 2:10 p.m.