Triple
T12817892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binnenhof area, The Hague |
E306448
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Torentje |
E36378
|
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: Torentje | Statement: [Binnenhof area, The Hague, hasPart, Torentje]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torentje Context triple: [Binnenhof area, The Hague, hasPart, Torentje]
-
A.
Torentje
chosen
Torentje is the small historic tower in The Hague that serves as the Dutch Prime Minister’s official office.
-
B.
Toorop
Toorop is the surname of Jan Toorop, a prominent Dutch-Indonesian painter associated with Symbolism and Art Nouveau.
-
C.
Torensluis
Torensluis is one of Amsterdam’s oldest and widest stone bridges, notable for its historic architecture and remnants of a former tower and prison cells.
-
D.
Tordino
Tordino is a river in the Abruzzo region of central Italy that flows through the city of Teramo before reaching the Adriatic Sea.
-
E.
Teuge
Teuge is a village in the Netherlands known for its small international airport and skydiving activities.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9d00088190ac0f5d60e1de7a7c |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ecee33c8190a6bf045731bb9326 |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 9, 2026, 5:31 p.m.