Triple
T18544405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rixheim |
E453184
|
entity |
| Predicate | distanceToMulhouseKm |
P132099
|
FINISHED |
| Object | approximately 5 |
—
|
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 5 | Statement: [Rixheim, distanceToMulhouseKm, approximately 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMulhouseKm Context triple: [Rixheim, distanceToMulhouseKm, approximately 5]
-
A.
distanceToLausanne
Indicates the measured distance between a given entity’s location and the city of Lausanne.
-
B.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
C.
distanceToZurich_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Zurich.
-
D.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
-
E.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
- 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534b9fa408190b5b397962920e439 |
completed | April 19, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69e469e274a48190a570b25cfef4d890 |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:38 a.m.