Triple
T9799204
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Goodyear Tire & Rubber Company |
E237791
|
entity |
| Predicate | hasTickerSymbol |
P1447
|
FINISHED |
| Object | GT |
E237791
|
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: [The Goodyear Tire & Rubber Company, hasTickerSymbol, GT]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GT Context triple: [The Goodyear Tire & Rubber Company, hasTickerSymbol, GT]
-
A.
GT
GT is a leading public research university in Atlanta, Georgia, renowned for its strong engineering, computing, and technology programs.
-
B.
GT
GT is the ISO 3166-1 alpha-2 country code for Guatemala, a Central American nation known for its Mayan heritage and diverse landscapes.
-
C.
GT
GT is a performance-oriented trim level commonly associated with sportier styling and enhanced powertrain features on vehicles like the Mercury Cougar.
-
D.
GT
chosen
GT is the stock ticker symbol for The Goodyear Tire & Rubber Company, a major American manufacturer of tires and rubber products.
-
E.
GD
GD is the abbreviation for Guangdong, a populous and economically significant coastal province in southern China.
- 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_69d1cc53c3dc819084a4d8b72164a172 |
completed | April 5, 2026, 2:43 a.m. |
Created at: March 30, 2026, 8:28 p.m.