Triple
T13808517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lom municipality |
E331819
|
entity |
| Predicate | hasNotableBuildingMaterial |
P104293
|
FINISHED |
| Object | wood |
—
|
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: wood | Statement: [Lom municipality, hasNotableBuildingMaterial, wood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableBuildingMaterial Context triple: [Lom municipality, hasNotableBuildingMaterial, wood]
-
A.
hasStationBuildingMaterial
Indicates that a station’s building is constructed from, or primarily composed of, a specified material.
-
B.
hasBuildingMaterialInHut
Indicates that a hut is constructed using a specified building material.
-
C.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
hasReconstructionMaterial
Indicates that something is associated with, composed of, or utilizes a particular material for its reconstruction.
-
E.
featuresMaterialType
chosen
Indicates that an entity is characterized by or incorporates a specific type of material.
- 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de026eae8481908b8880635e6a9152 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:12 p.m.