Triple
T5111558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sybill Trelawney |
E115223
|
entity |
| Predicate | teachesFromYear |
P61653
|
FINISHED |
| Object | 1980s |
—
|
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: 1980s | Statement: [Sybill Trelawney, teachesFromYear, 1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesFromYear Context triple: [Sybill Trelawney, teachesFromYear, 1980s]
-
A.
taughtThrough
Indicates that one entity provided instruction, education, or training to another entity by means of a specified method, medium, or intermediary.
-
B.
beganInYear
Indicates that an event, process, or state started in a specific calendar year.
-
C.
matriculationYear
Indicates the calendar year in which an individual formally enrolled or was admitted into an educational program or institution.
-
D.
studCareerStartYear
Indicates the calendar year in which a student's academic or educational career formally began.
-
E.
laterTypeStartYear
Indicates that the starting year of one type or classification occurs later in time than the starting year of another type or classification.
- 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_69bd4441d1648190a54a533895041987 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75ca57e881908242def2a032902e |
completed | March 20, 2026, 4:28 p.m. |
| PD | Predicate disambiguation | batch_69bd715fe3a8819087d3065adddba515 |
completed | March 20, 2026, 4:10 p.m. |
| PDg | Predicate description generation | batch_69bd72e1b7cc8190b2e621fdf8f22e38 |
completed | March 20, 2026, 4:16 p.m. |
Created at: March 20, 2026, 1:41 p.m.