Triple
T22154929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Titia de Lange |
E547510
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | de Lange |
E92052
|
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: de Lange | Statement: [Titia de Lange, familyName, de Lange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: de Lange Context triple: [Titia de Lange, familyName, de Lange]
-
A.
Lange Jan
Lange Jan is a famous tall church tower in Middelburg, the Netherlands, known as one of the country’s most prominent landmarks.
-
B.
Philip de Lange
Philip de Lange was an 18th-century Dutch-Danish architect known for shaping Copenhagen’s Baroque and Rococo architecture through numerous prominent public and military buildings.
-
C.
Nicolas de Lange
Nicolas de Lange was a notable figure in Lyon’s history, likely a local dignitary or benefactor, after whom the Montée Nicolas de Lange in the city is named.
-
D.
Lange Niezel
Lange Niezel is a narrow historic street in central Amsterdam’s Red Light District, known for its mix of tourist shops, bars, and traditional Dutch architecture.
-
E.
Lange
chosen
Lange is a German surname borne by numerous notable individuals across fields such as science, politics, arts, and sports.
- 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_69e11e3b52088190ad5df386d01eb2fb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129f7d034819092ecf9682e549c9a |
completed | April 28, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a970a95488190ac723daa646820d0 |
completed | May 18, 2026, 4:35 a.m. |
Created at: April 16, 2026, 8:33 p.m.