Triple
T23138098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabo Branco Lighthouse |
E577377
|
entity |
| Predicate | distanceToPontaDoSeixas |
P151054
|
FINISHED |
| Object | short distance |
—
|
LITERAL 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: short distance | Statement: [Cabo Branco Lighthouse, distanceToPontaDoSeixas, short distance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPontaDoSeixas Context triple: [Cabo Branco Lighthouse, distanceToPontaDoSeixas, short distance]
-
A.
distanceFromPorto
Indicates the measured distance between a given place or entity and the city of Porto.
-
B.
distanceToCascaisApproxKm
Indicates the approximate distance, measured in kilometers, from a given location to Cascais.
-
C.
distanceFromSantarém
Indicates the measured distance between a given entity or location and the city of Santarém.
-
D.
distanceToPraia
Indicates the measured distance between an entity and the location Praia.
-
E.
distanceFromLisbon
Indicates the measured spatial distance between a given entity’s location and the city of Lisbon.
- F. None of above. chosen
Provenance (4 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e8d6c20819085e8c2f97bc7fd5d |
completed | April 29, 2026, 4:52 a.m. |
| PD | Predicate disambiguation | batch_69ef89f83b108190aaaa1db6221fc163 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 4 p.m.