Triple
T29076847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montrouge town hall |
E735963
|
entity |
| Predicate | hasSuburbRelationTo |
P193379
|
FINISHED |
| Object | Paris |
—
|
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: Paris | Statement: [Montrouge town hall, hasSuburbRelationTo, Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSuburbRelationTo Context triple: [Montrouge town hall, hasSuburbRelationTo, Paris]
-
A.
hasSuburbAlong
Indicates that a larger area or route is associated with, or passes by, one or more suburbs located along its extent.
-
B.
associatedWithSuburb
Indicates a relationship where something is linked or connected to a particular suburb, such as being located in, serving, or otherwise related to that suburb.
-
C.
hasNearbySuburb
Indicates that one location has another location as a suburb situated in close geographic proximity.
-
D.
connectsToSuburb
Indicates that one entity has a direct connection or link to a suburban area, such as via transport, infrastructure, or adjacency.
-
E.
effectivelySuburbOf
chosen
Indicates that one place functions in practice as a suburb of another place, even if it may not be formally designated as such.
- F. None of above.
Provenance (3 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_69f077e9b0a48190bb79548279cb7f64 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
Created at: April 28, 2026, 10:23 a.m.