Triple
T9799194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Goodyear Tire & Rubber Company |
E237791
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Fulda |
E161070
|
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: Fulda | Statement: [The Goodyear Tire & Rubber Company, brand, Fulda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fulda Context triple: [The Goodyear Tire & Rubber Company, brand, Fulda]
-
A.
Fulda
Fulda is a major river in central Germany that flows through the state of Hesse and joins the Werra to form the Weser.
-
B.
Fulda
chosen
Fulda is a historic city in central Germany known for its Baroque architecture and former status as an important monastic and ecclesiastical center.
-
C.
Merseburg
Merseburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and role as an important cultural and administrative center on the River Saale.
-
D.
Hildesheim
Hildesheim is a historic city in northern Germany renowned for its medieval architecture and UNESCO-listed Romanesque churches.
-
E.
Fritzlar
Fritzlar is a historic town in northern Hesse, Germany, known for its well-preserved medieval old town and its significance in early German Christian history.
- 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda627cc2c81909ccc2e26751f5e85 |
completed | April 1, 2026, 11:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1c445c81481908700ffde5dd28936 |
completed | April 5, 2026, 2:09 a.m. |
Created at: March 30, 2026, 8:28 p.m.