Triple
T3164924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hettange-Grande |
E66187
|
entity |
| Predicate | distanceToThionvilleKm |
P46459
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Hettange-Grande, distanceToThionvilleKm, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToThionvilleKm Context triple: [Hettange-Grande, distanceToThionvilleKm, approximately 10]
-
A.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
-
B.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
C.
distanceToLuxembourgCityKilometers
Indicates the physical distance, measured in kilometers, between the given entity and Luxembourg City.
-
D.
cityDistanceFromBrussels_km
Indicates the distance, measured in kilometers, between a given city and Brussels.
-
E.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6424ee48190a29891ffbbc3811d |
completed | March 8, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfe0a948190928f2201d671c654 |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada148e9108190b363dd0f1a94ac8e |
completed | March 8, 2026, 4:18 p.m. |
Created at: March 8, 2026, 3:06 p.m.