Triple
T4284576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Levi Woodbury |
E97235
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Levi |
E64668
|
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: Levi | Statement: [Levi Woodbury, givenName, Levi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Levi Context triple: [Levi Woodbury, givenName, Levi]
-
A.
Levi
Levi is the surname of Primo Levi, the renowned Italian Jewish chemist and writer best known for his memoirs about surviving the Auschwitz concentration camp.
-
B.
Levi
chosen
Levi is a biblical patriarch, one of the twelve sons of Jacob and ancestor of the Israelite tribe of Levi, traditionally associated with priestly duties.
-
C.
Levi
Levi is a popular ski resort and tourist destination in Finnish Lapland, known for its extensive slopes, winter sports, and vibrant holiday village.
-
D.
Pepe Jeans
Pepe Jeans is a British denim and casualwear fashion brand known for its trendy jeans and youthful, urban style.
-
E.
Brother
Brother is a Japanese multinational electronics and electrical equipment company best known for its printers, sewing machines, and other office and home devices.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3503c062c81908f9a9eeab5381ec9 |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7c2023c8190a2359f8cabcecd2c |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.