Triple
T17339989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avco |
E421040
|
entity |
| Predicate | hasBrand |
P1500
|
FINISHED |
| Object | Lycoming |
E857291
|
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: Lycoming | Statement: [Avco, hasBrand, Lycoming]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lycoming Context triple: [Avco, hasBrand, Lycoming]
-
A.
Lycoming
chosen
Lycoming is a major American manufacturer of piston aircraft engines widely used in general aviation and military trainer aircraft.
-
B.
Lycoming County
Lycoming County is a largely rural county in north-central Pennsylvania known for its county seat of Williamsport and its rich history tied to the lumber industry and outdoor recreation.
-
C.
Susquehanna County
Susquehanna County is a rural county in northeastern Pennsylvania known for its rolling hills, small towns, and proximity to the New York state border.
-
D.
Luzerne County
Luzerne County is a county in northeastern Pennsylvania known for its seat in Wilkes-Barre and its role in the historic anthracite coal mining region.
-
E.
Cambria County
Cambria County is a county in west-central Pennsylvania known historically for its coal mining and steel industries, with Johnstown as its largest city.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a14ec90819098db2ac0d58a53e1 |
completed | April 19, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c588a7081909ab108cb4adfedfe |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:44 a.m.