Triple
T29080924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | République (Paris Métro) |
E733977
|
entity |
| Predicate | hasNeighbouringStationOnLine 3 |
P195811
|
FINISHED |
| Object | Temple (Paris Métro) |
—
|
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: Temple (Paris Métro) | Statement: [République (Paris Métro), hasNeighbouringStationOnLine 3, Temple (Paris Métro)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNeighbouringStationOnLine 3 Context triple: [République (Paris Métro), hasNeighbouringStationOnLine 3, Temple (Paris Métro)]
-
A.
hasAdjacentStationOnLine 2
Indicates that one station is directly next to another station along Line 2 in the network.
-
B.
hasAdjacentStationOnLine 5
Indicates that one station is directly next to another station along line 5, with no other stations in between on that line.
-
C.
hasAdjacentStationOnLine 11
Indicates that one station is directly next to another station along Line 11, with no other station between them on that line.
-
D.
hasAdjacentStationOnLine 7bis
Indicates that one station is directly next to another station along metro line 7bis.
-
E.
hasAdjacentStationOnLine1
Indicates that one station is directly next to another station along Line 1 in the network.
- 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_69f05b0c0f28819086eae6e84f2ae472 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69fde9fc184c8190bebef35df0e76076 |
completed | May 8, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69fde6e5beb4819094945a695e961d88 |
completed | May 8, 2026, 1:36 p.m. |
| PDg | Predicate description generation | batch_69fde9fb68388190ada4a7018e2a2f76 |
completed | May 8, 2026, 1:49 p.m. |
Created at: April 28, 2026, 10:55 a.m.