Triple
T4197916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Hiddleston |
E85997
|
entity |
| Predicate | graduationYearFromRADA |
P54302
|
FINISHED |
| Object | 2005 |
—
|
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: 2005 | Statement: [Tom Hiddleston, graduationYearFromRADA, 2005]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: graduationYearFromRADA Context triple: [Tom Hiddleston, graduationYearFromRADA, 2005]
-
A.
matriculationYear
Indicates the calendar year in which an individual formally enrolled or was admitted into an educational program or institution.
-
B.
hasRankUponGraduation
Indicates that an entity is assigned a specific rank or standing at the time of its graduation.
-
C.
graduation
Indicates that an entity has successfully completed an educational program and received a corresponding degree or qualification.
-
D.
lastSittingYear
Indicates the specific year in which an entity most recently held a sitting, session, or term.
-
E.
degreeGrantedBy
Indicates that a specific academic degree was officially conferred by a particular granting institution.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0360bc8081908ceb2483eef89174 |
completed | March 9, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69af01959c4881909eb1adcb3bdadbe6 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af02e4e2308190b527e3b78eb8fa71 |
completed | March 9, 2026, 5:27 p.m. |
Created at: March 9, 2026, 3:48 p.m.