Triple
T15215719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mattersburg |
E363631
|
entity |
| Predicate | distanceToEisenstadt_km |
P117553
|
FINISHED |
| Object | approximately 15 |
—
|
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: approximately 15 | Statement: [Mattersburg, distanceToEisenstadt_km, approximately 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToEisenstadt_km Context triple: [Mattersburg, distanceToEisenstadt_km, approximately 15]
-
A.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceFromBratislava_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Bratislava.
-
D.
distanceToBern_km
Indicates the distance, measured in kilometers, between an entity’s location and the city of Bern.
-
E.
distanceToLinz_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Linz.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2ca6148190967c319728ec3661 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:11 a.m.