Triple
T21669123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin Briscoe |
E534798
|
entity |
| Predicate | activeYearsInAutomobileIndustry |
P34676
|
FINISHED |
| Object | circa 1890s–1910s |
—
|
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: circa 1890s–1910s | Statement: [Benjamin Briscoe, activeYearsInAutomobileIndustry, circa 1890s–1910s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInAutomobileIndustry Context triple: [Benjamin Briscoe, activeYearsInAutomobileIndustry, circa 1890s–1910s]
-
A.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
B.
activeYearsWith
Indicates the span of time during which an entity was actively engaged in a particular role, activity, or association with another entity.
-
C.
enteredAutomobileProduction
Indicates that an entity began manufacturing automobiles as a commercial or industrial activity.
-
D.
activeInYears
chosen
Indicates that an entity was active or operational during the specified years or year range.
-
E.
racingActiveYears
Indicates the span of years during which an entity was actively involved in racing competitions.
- 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_69e0c46898008190aa618a4af55bd1ee |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c0e008c8190a549d275b1c98e0b |
completed | April 27, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69e6968abfdc81909cf9e0bd72db9eca |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:37 p.m.