Triple
T4479505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Baptiste Le Prince |
E100093
|
entity |
| Predicate | travelPeriod |
P302
|
FINISHED |
| Object | 1758-1763 |
—
|
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: 1758-1763 | Statement: [Jean-Baptiste Le Prince, travelPeriod, 1758-1763]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelPeriod Context triple: [Jean-Baptiste Le Prince, travelPeriod, 1758-1763]
-
A.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
B.
timeTravelFrom
Indicates a relationship where an entity initiates time travel starting from a specific time or temporal location.
-
C.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
D.
flightPeriod
Indicates the time span during which a flight occurs or is scheduled to operate.
-
E.
travelPattern
Indicates the typical routes, frequencies, and behaviors associated with how an entity moves or travels between locations.
- 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_69b34553cbe48190afa8ac1cac285b86 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35728ed508190ba0e882fa62d8848 |
completed | March 13, 2026, 12:15 a.m. |
| PD | Predicate disambiguation | batch_69b3563d63008190816e37027e761375 |
completed | March 13, 2026, 12:11 a.m. |
Created at: March 12, 2026, 11:35 p.m.