Triple
T20541536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas |
E504346
|
entity |
| Predicate | worksWith |
P398
|
FINISHED |
| Object | Lambert |
—
|
NE NERFINISHED |
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: Lambert | Statement: [Dallas, worksWith, Lambert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lambert Context triple: [Dallas, worksWith, Lambert]
-
A.
Lambert
Lambert is a masculine given name of Germanic origin, historically borne by various saints, nobles, and notable figures in Europe.
-
B.
Lambert Orkis
Lambert Orkis is an American classical pianist best known for his long-standing collaborations with leading violinists and cellists, including Anne-Sophie Mutter and Mstislav Rostropovich.
-
C.
Lampson
Lampson is a surname most notably associated with American politician Nick Lampson, a former U.S. Representative from Texas.
-
D.
Lemery
Lemery is a coastal municipality in the province of Batangas in the Philippines, known for its commercial activity and proximity to Taal Lake and Volcano.
-
E.
Laubach
Laubach is a small historic town in the German state of Hesse, known for its medieval old town and Laubach Castle.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69e0b4b476648190bc6019622ae54d3c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a292dd1c8190b5c3031f3b44eb52 |
completed | April 20, 2026, 10:02 p.m. |
Created at: April 16, 2026, 11:37 a.m.