Triple
T28930169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Feira de Santana |
E733758
|
entity |
| Predicate | distanceToSalvador_km |
P202109
|
FINISHED |
| Object | about 100 |
—
|
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 100 | Statement: [Feira de Santana, distanceToSalvador_km, about 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSalvador_km Context triple: [Feira de Santana, distanceToSalvador_km, about 100]
-
A.
distanceFromSanSalvador
Indicates the measured distance between an entity and the location of San Salvador.
-
B.
distanceToRioDeJaneiroCity
Indicates the physical distance between a given entity’s location and the city of Rio de Janeiro.
-
C.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
D.
distanceToGuayaquil_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Guayaquil.
-
E.
distanceFromVascoDaGama
Indicates the measured spatial distance between a given entity and Vasco da Gama.
- 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_69f05b0b49b08190b8994b339c7980f6 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_6a005247dba08190acadf962bcefe4a0 |
completed | May 10, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_6a00519029848190a234358dfba45084 |
completed | May 10, 2026, 9:36 a.m. |
| PDg | Predicate description generation | batch_6a00524723408190b0294e87e5cf7715 |
completed | May 10, 2026, 9:39 a.m. |
Created at: April 28, 2026, 8:27 a.m.