Triple
T35680270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belgian railway line 96 |
E1030985
|
entity |
| Predicate | hasSectionParallelTo |
P201766
|
FINISHED |
| Object | HSL 1 |
—
|
NE NERFINISHED |
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: HSL 1 | Statement: [Belgian railway line 96, hasSectionParallelTo, HSL 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectionParallelTo Context triple: [Belgian railway line 96, hasSectionParallelTo, HSL 1]
-
A.
hasSectionAlong
Indicates that one entity includes or runs along a specific segment or portion of another entity.
-
B.
hasSectionIn
Indicates that one entity contains or includes another entity as a section or subdivision within it.
-
C.
isPartiallyParallelTo
Indicates that two entities are aligned in roughly the same direction but not exactly parallel, sharing only a partial or approximate parallel relationship.
-
D.
has2DSection
Indicates that one entity represents a two-dimensional cross-sectional view or slice of another entity.
-
E.
hasSectionOn
Indicates that one entity (typically a document or resource) contains a dedicated section or part that specifically addresses or discusses another entity or topic.
- 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_69f76e0bb6608190ad3a1880be54a17d |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a001cc0ff588190bb7c8a6fd427d02b |
completed | May 10, 2026, 5:50 a.m. |
| PD | Predicate disambiguation | batch_6a001b3ea18c8190aeda7a32b2697490 |
completed | May 10, 2026, 5:44 a.m. |
| PDg | Predicate description generation | batch_6a001cc053ac8190927768a4ecb023b9 |
completed | May 10, 2026, 5:50 a.m. |
Created at: May 3, 2026, 4:05 p.m.