Triple
T37435289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 6 (Paris Métro) |
E930245
|
entity |
| Predicate | hasLoopShape |
P200445
|
FINISHED |
| Object | semi-circular |
—
|
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: semi-circular | Statement: [Line 6 (Paris Métro), hasLoopShape, semi-circular]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoopShape Context triple: [Line 6 (Paris Métro), hasLoopShape, semi-circular]
-
A.
hasLoopMode
Indicates that an entity operates or is configured in a mode where its behavior or process repeats in a loop.
-
B.
isLoopOf
Indicates that one entity represents a loop or cyclic structure corresponding to, derived from, or associated with another entity.
-
C.
hasLoopConfiguration
Indicates that an entity is associated with a specific loop setup or arrangement that defines how a loop operates or is structured.
-
D.
hasLoopRole
Indicates that an entity participates in a loop structure with a specific functional role within that loop.
-
E.
hasLoopNumber
Indicates that an entity is associated with a specific loop identifier or count, typically representing its position or iteration within a repeated cycle or process.
- 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_69f76ebfdcb8819098562ff3db673b04 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ff8aff48988190a48a440de8238ef9 |
completed | May 9, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69ff8a780404819082f48ceb21e7fe11 |
completed | May 9, 2026, 7:26 p.m. |
| PDg | Predicate description generation | batch_69ff8afe9d5c8190bd5ae434fc2b550b |
completed | May 9, 2026, 7:29 p.m. |
Created at: May 3, 2026, 4:17 p.m.