Triple
T36532175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quasi-Stellar Object |
E900473
|
entity |
| Predicate | timeVariability |
P44297
|
FINISHED |
| Object | variability on timescales from days to years |
—
|
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: variability on timescales from days to years | Statement: [Quasi-Stellar Object, timeVariability, variability on timescales from days to years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeVariability Context triple: [Quasi-Stellar Object, timeVariability, variability on timescales from days to years]
-
A.
timeSettingVariant
Indicates a relationship where one time setting is an alternative or modified version of another time setting.
-
B.
timeSensitivity
Indicates how strongly the relationship or action depends on, changes with, or is constrained by time.
-
C.
timePeriod
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
D.
timeDependence
chosen
Indicates that one entity’s state, value, or behavior is determined by or varies as a function of another entity over time.
-
E.
timeOfChange
Indicates the specific time at which a change, transition, or modification of a state or relationship occurs.
- 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_69f76e5fbb388190b70c4c15573c8143 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.