Triple
T15968678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werther (Westphalia) |
E387263
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | GT |
E406774
|
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: GT | Statement: [Werther (Westphalia), vehicleRegistrationCode, GT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GT Context triple: [Werther (Westphalia), vehicleRegistrationCode, GT]
-
A.
GT
GT is a performance-oriented trim level commonly associated with sportier styling and enhanced powertrain features on vehicles like the Mercury Cougar.
-
B.
GT
GT is a leading public research university in Atlanta, Georgia, renowned for its strong engineering, computing, and technology programs.
-
C.
GT
GT is the stock ticker symbol for The Goodyear Tire & Rubber Company, a major American manufacturer of tires and rubber products.
-
D.
GT
chosen
GT is the ISO 3166-1 alpha-2 country code for Guatemala, a Central American nation known for its Mayan heritage and diverse landscapes.
-
E.
GW
GW (Gesamtkatalog der Wiegendrucke) is a comprehensive scholarly catalog of incunabula, documenting books printed in Europe before 1501.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1572847f08190830e30125e829766 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe87149081909ac6129126f597c2 |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:54 a.m.