Triple
T7519005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Agustín Archaeological Park |
E177718
|
entity |
| Predicate | earliestOccupationPeriod |
P77435
|
FINISHED |
| Object | 1st century AD |
—
|
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: 1st century AD | Statement: [San Agustín Archaeological Park, earliestOccupationPeriod, 1st century AD]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earliestOccupationPeriod Context triple: [San Agustín Archaeological Park, earliestOccupationPeriod, 1st century AD]
-
A.
earliestOccupation
Indicates that the associated occupation is the first or earliest known job or professional role held by the person in question.
-
B.
earliestOccupationDate
Indicates the earliest known date on which an entity began a particular occupation or role.
-
C.
earliestMajorOccupation
Indicates the earliest significant occupation or professional role held by an entity in its life or career timeline.
-
D.
earlyOccupation
Indicates that an entity held a particular occupation or job during an early stage of its life or career.
-
E.
timePeriodOfEarlierOccupation
Indicates that one entity specifies the time span during which another entity was previously occupied or held a position before a later occupation.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f850c081909e697219071293fc |
completed | March 27, 2026, 9:26 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f574d8a8819095749518dad13791 |
completed | March 27, 2026, 9:24 p.m. |
Created at: March 27, 2026, 3:46 p.m.