Triple
T10828170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lambert Meertens |
E255545
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lambert |
E255544
|
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: Lambert | Statement: [Lambert Meertens, givenName, Lambert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lambert Context triple: [Lambert Meertens, givenName, Lambert]
-
A.
Lambert
chosen
Lambert is a masculine given name of Germanic origin, historically borne by various saints, nobles, and notable figures in Europe.
-
B.
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.
-
C.
Laudon
Laudon is a German-language surname most notably associated with the 18th-century Austrian field marshal Ernst Gideon von Laudon.
-
D.
Lindberg
Lindberg is a small municipality in the Regen district of Bavaria, Germany, known for its location in the Bavarian Forest region.
-
E.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734d3eab88190b30a3025b6b2b0bc |
completed | April 9, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8592d8f08190ac577395ad7cc557 |
completed | April 14, 2026, 6:21 p.m. |
Created at: April 8, 2026, 9:19 p.m.