Triple
T26818410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauss normal chron |
E675178
|
entity |
| Predicate | hasChronCode |
P77986
|
FINISHED |
| Object | C2An |
—
|
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: C2An | Statement: [Gauss normal chron, hasChronCode, C2An]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChronCode Context triple: [Gauss normal chron, hasChronCode, C2An]
-
A.
hasChronologyStatus
Indicates the temporal or sequential status of something within a chronology, such as whether its position or order in time is known, uncertain, or otherwise classified.
-
B.
hasMagneticChronCode
chosen
Indicates that an entity is associated with a specific magnetic chron code used to encode temporal or time-related information.
-
C.
hasChronologyItem
Indicates that something is associated with a specific item or entry within an ordered chronological sequence or timeline.
-
D.
hasChronologicalPhase
Indicates that something is associated with, or occurs during, a specific chronological phase or time period in a sequence of development or events.
-
E.
hasChronologyRelation
Indicates a temporal ordering relationship between entities, specifying how one relates to another in time (e.g., before, after, or overlapping).
- 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_69eee9b6b28481909332f83eb17e5170 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 27, 2026, 4:53 a.m.