Triple
T4688749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stockholm Arlanda Airport |
E103982
|
entity |
| Predicate | distanceFromUppsala_km |
P58802
|
FINISHED |
| Object | about 28 |
—
|
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 28 | Statement: [Stockholm Arlanda Airport, distanceFromUppsala_km, about 28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromUppsala_km Context triple: [Stockholm Arlanda Airport, distanceFromUppsala_km, about 28]
-
A.
distanceFromStockholmCityCentre
Indicates the measured distance between a given location and the center of Stockholm city.
-
B.
distanceToUppsalaByRail
Indicates the measured distance between a given place and Uppsala when traveling by railway.
-
C.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
-
D.
distanceFromOslo
Indicates the spatial distance between a given entity’s location and the city of Oslo.
-
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_69bd43df91f481908e9add1b617b60ef |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd66059bfc8190885d26d05dd38df1 |
completed | March 20, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69bd6219da948190bbbb50f08573ab4d |
completed | March 20, 2026, 3:04 p.m. |
| PDg | Predicate description generation | batch_69bd660414b08190ab37467755d383b5 |
completed | March 20, 2026, 3:21 p.m. |
Created at: March 20, 2026, 1:16 p.m.