Triple
T9400966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bessemer steel works at Harrisburg, Pennsylvania |
E226467
|
entity |
| Predicate | outputUse |
P64275
|
FINISHED |
| Object | railroad tracks |
—
|
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: railroad tracks | Statement: [Bessemer steel works at Harrisburg, Pennsylvania, outputUse, railroad tracks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: outputUse Context triple: [Bessemer steel works at Harrisburg, Pennsylvania, outputUse, railroad tracks]
-
A.
outputUsedBy
chosen
Indicates that a particular output produced by one entity is consumed, referenced, or otherwise utilized by another entity.
-
B.
actualUse
Indicates that an entity is currently being used or utilized in practice, as opposed to being merely available, planned, or potential.
-
C.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
D.
programUse
Indicates that one entity uses, employs, or makes use of a particular program or software application.
-
E.
usedFor
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
- 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_69ca843170f88190800a8ab2b5fc568e |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5157d66c819094c18f680c7093f7 |
completed | April 1, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69cca545b2448190a4297312e39c21ac |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:46 p.m.