Triple
T9280664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roy Wood Jr. |
E223057
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Wood |
E109794
|
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: Wood | Statement: [Roy Wood Jr., familyName, Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wood Context triple: [Roy Wood Jr., familyName, Wood]
-
A.
Wood
chosen
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
-
B.
Wood Wood
Wood Wood is a small rural locality in New South Wales, Australia, situated within the jurisdiction of the Murray River Council.
-
C.
Wooden
Wooden is a surname most famously associated with John Wooden, the legendary American college basketball coach known for his success at UCLA.
-
D.
Norsey Wood
Norsey Wood is an ancient woodland and designated Local Nature Reserve near Billericay in Essex, known for its rich biodiversity and archaeological features.
-
E.
Lenswood
Lenswood is a small rural town in South Australia's Adelaide Hills region, known for its cool-climate orchards and scenic vineyards.
- 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_69ca842123588190b3f2e1a69037d141 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07cd9a1c8190af0521baa428ce10 |
completed | April 1, 2026, 11:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b1fef1508190a9bf1a55dd39c0ac |
completed | April 4, 2026, 6:38 a.m. |
Created at: March 30, 2026, 7:34 p.m.