Triple
T20615990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strépy-Thieu boat lift |
E506567
|
entity |
| Predicate | hasNumberOfCaissons |
P91072
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Strépy-Thieu boat lift, hasNumberOfCaissons, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCaissons Context triple: [Strépy-Thieu boat lift, hasNumberOfCaissons, 2]
-
A.
numberOfCaissons
chosen
Indicates the quantity of caissons associated with or used in relation to a given entity or structure.
-
B.
hasTwinCaissons
Indicates that something is equipped with or associated with a pair of caissons functioning together as twins.
-
C.
hasNumberOfPiersInWater
Indicates the quantity of piers that extend into or are situated within a body of water.
-
D.
hasPylonCount
Indicates the relationship specifying how many pylons are associated with or present in a given entity.
-
E.
hasNumberOfEnceintes
Indicates the relationship that specifies how many defensive walls or enclosing fortification layers an entity possesses.
- 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_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aadbb3ac81908145f57fd6a94256 |
completed | April 20, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69e5a00c43308190b7ea58d559257e07 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:41 a.m.