Triple
T2327080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sam Wood |
E48311
|
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: [Sam Wood, familyName, Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wood Context triple: [Sam Wood, 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.
Oak
Oak is the professional name of Warren “Oak” Felder, a Grammy-nominated songwriter and record producer known for his work with major contemporary pop and R&B artists.
-
C.
Clinch Leatherwood
Clinch Leatherwood is the ruthless outlaw gunslinger who serves as the main antagonist in the comedy Western film "A Million Ways to Die in the West."
-
D.
How Wood
How Wood is a residential suburb and railway-served locality near St Albans in Hertfordshire, England.
-
E.
Lignum vitae
Lignum vitae is a dense, extremely hard tropical hardwood tree native to the Caribbean, renowned for its durable wood and medicinal resin.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc64c7f1881909b0d847f7782e803 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae897243c48190a18b0e02ad664ead |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:50 p.m.