Triple
T733608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valparaíso |
E14881
|
entity |
| Predicate | distanceToSantiago_km |
P19519
|
FINISHED |
| Object | about 120 |
—
|
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: about 120 | Statement: [Valparaíso, distanceToSantiago_km, about 120]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSantiago_km Context triple: [Valparaíso, distanceToSantiago_km, about 120]
-
A.
distanceFromJuanFernandezIslands_km
Indicates the distance, measured in kilometers, between an entity and the Juan Fernández Islands.
-
B.
distanceToSouthAmerica
Indicates the spatial distance between a given entity’s location and the continent of South America.
-
C.
distanceFromCusco
Indicates the measured spatial distance between a given location or entity and the city of Cusco.
-
D.
distanceToColombo
Indicates the measured or calculated spatial distance between a given entity’s location and the city of Colombo.
-
E.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
- 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_69a4934d9930819099eed80096b0597d |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a66820548190b373deb117187c2c |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4fafee081909bf356854c09aaff |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a66658948190bdae6e521951954f |
completed | March 1, 2026, 8:49 p.m. |
Created at: March 1, 2026, 7:37 p.m.