Triple
T5486849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ribatejo |
E123602
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Entroncamento |
E374173
|
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: Entroncamento | Statement: [Ribatejo, hasCity, Entroncamento]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Entroncamento Context triple: [Ribatejo, hasCity, Entroncamento]
-
A.
Entroncamento
chosen
Entroncamento is a Portuguese railway junction city in the Centro Region, known for its strategic location on the country’s main rail lines.
-
B.
Encruzilhada
Encruzilhada is a neighborhood in the city of Recife, Brazil, known for its busy commercial areas and urban residential character.
-
C.
Felling
Felling is a town in Tyne and Wear, England, situated on the south bank of the River Tyne near Gateshead and Newcastle upon Tyne.
-
D.
Divisadero
Divisadero is a nonlinear, character-driven novel by Michael Ondaatje that interweaves past and present across California and France to explore memory, identity, and the fractures within a makeshift family.
-
E.
Divisadero
Divisadero is a popular lookout and tourist stop in Mexico’s Copper Canyon region, known for its dramatic canyon views and access to scenic train routes.
- 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.