Triple
T2207184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skarżysko-Kamienna |
E50827
|
entity |
| Predicate | distanceToWarsaw |
P37440
|
FINISHED |
| Object | approximately 150 km |
—
|
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 150 km | Statement: [Skarżysko-Kamienna, distanceToWarsaw, approximately 150 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToWarsaw Context triple: [Skarżysko-Kamienna, distanceToWarsaw, approximately 150 km]
-
A.
distanceToKraków_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Kraków.
-
B.
distanceToKatowice
Indicates the spatial distance between a given entity and the city of Katowice.
-
C.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
D.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
E.
distanceToBucharest
Indicates the physical distance between a given location and the city of Bucharest.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc1b912c08190b9d7bc9230e49d1d |
completed | March 7, 2026, 6:12 a.m. |
Created at: March 4, 2026, 7:46 p.m.