Triple
T623111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reissner–Nordström metric |
E14555
|
entity |
| Predicate | hasKillingVectorField |
P2894
|
FINISHED |
| Object | \partial_t |
—
|
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: \partial_t | Statement: [Reissner–Nordström metric, hasKillingVectorField, \partial_t]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKillingVectorField Context triple: [Reissner–Nordström metric, hasKillingVectorField, \partial_t]
-
A.
hasNumberOfKillingVectors
Indicates the relationship that specifies how many Killing vector fields (symmetries of the metric) a given geometric or physical system possesses.
-
B.
hasVector
chosen
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector space.
-
C.
killedBy
Indicates that one entity caused the death of another entity.
-
D.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
E.
hasCoordinateSingularity
Indicates that something possesses a point or region where its coordinate description becomes undefined or degenerate, even if the underlying object or space may remain well-behaved.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e41753881909f0faed720cc31bc |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49d0069d0819087c83b608f6fc053 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.