Triple
T219591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American South (colonial and early national periods) |
E4184
|
entity |
| Predicate | hasTemporalExtent |
P302
|
FINISHED |
| Object | 17th century |
—
|
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: 17th century | Statement: [American South (colonial and early national periods), hasTemporalExtent, 17th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTemporalExtent Context triple: [American South (colonial and early national periods), hasTemporalExtent, 17th century]
-
A.
temporalRelation
Indicates a relationship that specifies how two events or states are positioned relative to each other in time (e.g., before, after, or overlapping).
-
B.
timePeriodCoveredTo
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
-
C.
refersToPeriod
Indicates that one entity designates, references, or is associated with a specific time period or interval.
-
D.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
E.
hasLongTermDatasetSince
Indicates that an entity has maintained or used a particular dataset continuously starting from a specified point in time.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c6d0fa08190810139b14f4851bc |
completed | Feb. 28, 2026, 3:09 a.m. |
| PD | Predicate disambiguation | batch_69a25b5357bc8190b29a48e3053fb76d |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.