Triple
T1463175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faraday's law of induction |
E31559
|
entity |
| Predicate | includesSymbol |
P5716
|
FINISHED |
| Object | emf |
—
|
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: emf | Statement: [Faraday's law of induction, includesSymbol, emf]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesSymbol Context triple: [Faraday's law of induction, includesSymbol, emf]
-
A.
symbolicallyUses
Indicates that one entity employs another as a symbol or representation to convey meaning, ideas, or associations rather than for its literal or practical function.
-
B.
containsSymbolicAct
Indicates that one entity includes or incorporates a symbolic action or gesture associated with another entity.
-
C.
hasSymbolNamedAfter
Indicates that one entity has a symbol whose name is derived from or dedicated to another entity.
-
D.
includesConstant
Indicates that one entity contains or explicitly references a specific constant value within its definition or structure.
-
E.
containsCharacter
chosen
Indicates that one entity includes a specific character as part of its content or composition.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b89708819084fb9ba4ff293b8b |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.