Triple
T23167754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lense–Thirring effect |
E578757
|
entity |
| Predicate | largerNear |
P151188
|
FINISHED |
| Object | rapidly rotating neutron stars |
—
|
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: rapidly rotating neutron stars | Statement: [Lense–Thirring effect, largerNear, rapidly rotating neutron stars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: largerNear Context triple: [Lense–Thirring effect, largerNear, rapidly rotating neutron stars]
-
A.
meetsNear
Indicates that two entities meet or come together at a location that is in close proximity to a specified reference point or area.
-
B.
near
Indicates that one entity is located at a short distance from another entity in space or position.
-
C.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
D.
youngerNear
Indicates that one entity is younger than another and is located physically close to that other entity.
-
E.
closerTo
Indicates that one entity is at a smaller distance to a reference entity than another entity is.
- F. None of above. chosen
Provenance (4 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_69e245fc75348190a0288401044c8af8 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18f2d51288190af0d5747090d8e5d |
completed | April 29, 2026, 4:55 a.m. |
| PD | Predicate disambiguation | batch_69ef89ff76808190808ee4ad9dea776b |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b75e2708190ba48875e36f983bc |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 4:03 p.m.