Triple
T739009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Paper |
E15200
|
entity |
| Predicate | usesRawMaterial |
P1272
|
FINISHED |
| Object | wood fiber |
—
|
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 fiber | Statement: [International Paper, usesRawMaterial, wood fiber]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRawMaterial Context triple: [International Paper, usesRawMaterial, wood fiber]
-
A.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
B.
sourceMaterialType
Indicates the type or category of material from which something originates or is derived.
-
C.
usedComponent
Indicates that one entity has employed or incorporated another entity as a component in its structure, function, or operation.
-
D.
usesIngredient
Indicates that one entity employs or incorporates another entity as an ingredient in its composition or creation.
-
E.
hasMaterialType
Indicates that something is composed of, made from, or characterized by 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_69a49358aa308190adbc9b5a0a2adcf9 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64adf2c81908e48090be35dd9d9 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a4fc734c81908fbd36386d5746d6 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.