Triple
T14963873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vecht (Utrecht) |
E373135
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Nigtevecht |
E1046461
|
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: Nigtevecht | Statement: [Vecht (Utrecht), flowsThrough, Nigtevecht]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nigtevecht Context triple: [Vecht (Utrecht), flowsThrough, Nigtevecht]
-
A.
Nigtevecht
chosen
Nigtevecht is a small village in the Dutch province of Utrecht, known for its historic riverside setting along the river Vecht.
-
B.
Nachtwey
Nachtwey is the surname of James Nachtwey, a renowned American photojournalist celebrated for his powerful war and conflict photography.
-
C.
Nitelva
Nitelva is a river in southeastern Norway that flows through the Romerike region before joining the larger Glomma river.
-
D.
Nattheim
Nattheim is a municipality in the Heidenheim district of Baden-Württemberg in southern Germany.
-
E.
Elbling
Elbling is an ancient white wine grape variety primarily cultivated in Germany and Luxembourg, known for producing light, crisp, and high-acidity wines.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6d0487c8190b7754af8c5014b37 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bdeec408190893d1db9254da24e |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:40 a.m.