Triple
T4752184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alicia Vikander |
E105501
|
entity |
| Predicate | activeYearsInCareerStart |
P18004
|
FINISHED |
| Object | 2000s |
—
|
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: 2000s | Statement: [Alicia Vikander, activeYearsInCareerStart, 2000s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeYearsInCareerStart Context triple: [Alicia Vikander, activeYearsInCareerStart, 2000s]
-
A.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
B.
activeYearsInSport
Indicates the span of years during which an entity actively participated in a particular sport.
-
C.
activeYearsEndTime
Indicates the point in time when an entity’s period of activity or operation comes to an end.
-
D.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
E.
firstYearPlayed
Indicates the year in which an entity (such as a person or team) first began participating in the specified activity or domain.
- 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_69bd43f07fa48190954317d01600994a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64e5fba88190b1f28d1b0eed3f8e |
completed | March 20, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69bd6223defc8190823665a6592c1154 |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:20 p.m.