Triple
T9766272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betuwe |
E236999
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | Tiel |
E94139
|
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: Tiel | Statement: [Betuwe, hasSubregion, Tiel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tiel Context triple: [Betuwe, hasSubregion, Tiel]
-
A.
Tiel
chosen
Tiel is a historic Dutch city situated along the River Waal, known for its fruit cultivation and role as a regional trade center in the province of Gelderland.
-
B.
Tiba
Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
-
C.
Tachov
Tachov is a town in western Czechia that serves as an administrative center and local hub within the Plzeň Region.
-
D.
Tschira
Tschira is a German surname most notably associated with Klaus Tschira, a co-founder of the software company SAP and a prominent philanthropist in science and education.
-
E.
Tegüder
Tegüder (also known as Ahmad Tegüder) was a 13th-century Ilkhanid ruler of Persia who converted to Islam and briefly reigned as a Mongol khan.
- 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_69ca84d831b8819090322686b47887ce |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda0a15e408190909745cb1c30937d |
completed | April 1, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bcf965e88190b505ce160f77e9b7 |
completed | April 5, 2026, 1:38 a.m. |
Created at: March 30, 2026, 8:25 p.m.