Triple
T6198832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Wooden |
E138577
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Wooden
Wooden is a surname most famously associated with John Wooden, the legendary American college basketball coach known for his success at UCLA.
|
E575059
|
NE FINISHED |
How this triple was built (4 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: Wooden | Statement: [John Wooden, familyName, Wooden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wooden Context triple: [John Wooden, familyName, Wooden]
-
A.
Wood
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
-
B.
Lenswood
Lenswood is a small rural town in South Australia's Adelaide Hills region, known for its cool-climate orchards and scenic vineyards.
-
C.
Threepwood
Threepwood is the aristocratic family name of the eccentric Blandings Castle clan in P. G. Wodehouse’s comic novels.
-
D.
De Wood
De Wood is a specific variant or form of wood distinguished from the general category of wood materials.
-
E.
Maderas
Maderas is a stratovolcano on Ometepe Island in Lake Nicaragua, known for its cloud forest, crater lagoon, and popular hiking trails.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Wooden Triple: [John Wooden, familyName, Wooden]
Generated description
Wooden is a surname most famously associated with John Wooden, the legendary American college basketball coach known for his success at UCLA.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wooden Target entity description: Wooden is a surname most famously associated with John Wooden, the legendary American college basketball coach known for his success at UCLA.
-
A.
Wood
Wood is a common English surname with historical roots in Britain, often originally referring to someone who lived or worked near a forest.
-
B.
Lenswood
Lenswood is a small rural town in South Australia's Adelaide Hills region, known for its cool-climate orchards and scenic vineyards.
-
C.
Threepwood
Threepwood is the aristocratic family name of the eccentric Blandings Castle clan in P. G. Wodehouse’s comic novels.
-
D.
De Wood
De Wood is a specific variant or form of wood distinguished from the general category of wood materials.
-
E.
Maderas
Maderas is a stratovolcano on Ometepe Island in Lake Nicaragua, known for its cloud forest, crater lagoon, and popular hiking trails.
- F. None of above. chosen
Provenance (5 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_69c008acbea48190991c6b834bb45d65 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06253534c8190aafe70a6cf5a67ec |
completed | March 22, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16f302984819089c6a17dda476a4b |
completed | March 23, 2026, 4:49 p.m. |
| NEDg | Description generation | batch_69c1c9a4bdd481909f836be4167befea |
completed | March 23, 2026, 11:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c1ca257cb88190bee32a8ecb15c02b |
completed | March 23, 2026, 11:17 p.m. |
Created at: March 22, 2026, 4:20 p.m.