Triple
T1937503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GTE |
E41475
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | GTE |
E41475
|
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: GTE | Statement: [GTE, shortName, GTE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GTE Context triple: [GTE, shortName, GTE]
-
A.
GTE
chosen
GTE (General Telephone & Electronics Corporation) was a major U.S. telecommunications company that became one of the largest local telephone service providers before ultimately merging into Verizon.
-
B.
GTS
GTS is a high-performance, sport-oriented variant of the Holden Monaro produced by Holden’s performance division.
-
C.
GTS
GTS is an abbreviation commonly used for the Global Telecommunication System, an international network for exchanging meteorological data.
-
D.
Genesys
Genesys is a global customer experience and contact center technology company known for its cloud-based solutions that help businesses manage and optimize customer interactions.
-
E.
Fitel
Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
- 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2c752fc81909baf38ff1cdeb18c |
completed | March 7, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3f6285c8190925af156f49cf9a2 |
completed | March 8, 2026, 10:11 p.m. |
Created at: March 4, 2026, 7:36 p.m.