Triple
T2723051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Hardy |
E60124
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hardy |
E120385
|
NE 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: Hardy | Statement: [Thomas Hardy, familyName, Hardy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hardy Context triple: [Thomas Hardy, familyName, Hardy]
-
A.
Hardy
chosen
Hardy is a surname most famously associated with the English mathematician G. H. Hardy, known for his contributions to number theory and mathematical analysis.
-
B.
Louis Thomas Hardy
Louis Thomas Hardy is the eldest son of English actor Tom Hardy.
-
C.
Edward Thomas Hardy
Edward Thomas Hardy is an English actor and producer known for his intense, transformative performances in films such as Inception, Mad Max: Fury Road, and The Dark Knight Rises, as well as the TV series Peaky Blinders.
-
D.
Ted Hardie
Ted Hardie is an Internet engineering expert and long-time IETF leader known for his work on real-time communications and web technologies.
-
E.
Aldous
Aldous is a masculine given name most famously borne by the English writer and philosopher Aldous Huxley.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdaca581881908fe8d3d820f839b7 |
completed | March 7, 2026, 7:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afb6939a50819087ac2c55337ceae3 |
completed | March 10, 2026, 6:13 a.m. |
Created at: March 6, 2026, 9:55 p.m.