Triple
T27768812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauren Lane |
E701679
|
entity |
| Predicate | yearsActiveOnTheNanny |
P143955
|
FINISHED |
| Object | 1993–1999 |
—
|
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: 1993–1999 | Statement: [Lauren Lane, yearsActiveOnTheNanny, 1993–1999]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveOnTheNanny Context triple: [Lauren Lane, yearsActiveOnTheNanny, 1993–1999]
-
A.
yearsActiveInStory
Indicates the span of years during which the entity is active or participates within the context of the story.
-
B.
activeYearsWith
chosen
Indicates the span of time during which an entity was actively engaged in a particular role, activity, or association with another entity.
-
C.
activeYearsInFilm
Indicates the span of years during which an entity was actively involved in film-related work or roles.
-
D.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
E.
yearsActiveAtNASA
Indicates the span of years during which an entity was actively involved with or employed by NASA.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f6379527cc8190b835dcf330096a64 |
completed | May 2, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69f63188e7408190af8ce8b93d128c63 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 27, 2026, 4:33 p.m.