Triple
T6396954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | anti-de Sitter space |
E143964
|
entity |
| Predicate | hasKillingVectorsNumber |
P14256
|
FINISHED |
| Object | d(d+1)/2 |
—
|
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: d(d+1)/2 | Statement: [anti-de Sitter space, hasKillingVectorsNumber, d(d+1)/2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKillingVectorsNumber Context triple: [anti-de Sitter space, hasKillingVectorsNumber, d(d+1)/2]
-
A.
hasNumberOfKillingVectors
chosen
Indicates the relationship that specifies how many Killing vector fields (symmetries of the metric) a given geometric or physical system possesses.
-
B.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
C.
hasVector
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector space.
-
D.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
-
E.
considersKilling
Indicates that one entity is contemplating or evaluating the possibility of killing another entity.
- 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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c068953968819083a94f5de3e11819 |
completed | March 22, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69c060f25c088190b433f78553ff1d84 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:35 p.m.