Triple
T18623180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kendall County |
E455207
|
entity |
| Predicate | ISO3166-2 |
P189
|
FINISHED |
| Object | US-TX |
—
|
NE NERFINISHED |
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: US-TX | Statement: [Kendall County, ISO3166-2, US-TX]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: US-TX Context triple: [Kendall County, ISO3166-2, US-TX]
-
A.
TX
TX is the IATA airline designator assigned to Air Caraïbes, a French Caribbean airline.
-
B.
TX
TX is the commonly used abbreviation for the Tsukuba Express, a Japanese railway line connecting Akihabara in Tokyo with Tsukuba in Ibaraki Prefecture.
-
C.
Como, Texas
Como, Texas is a small rural town located in Hopkins County in northeastern Texas, known for its close-knit community and agricultural surroundings.
-
D.
Texas
chosen
Texas is the second-largest U.S. state by both area and population, known for its diverse landscapes, major cities like Houston and Dallas, and significant cultural and economic influence.
-
E.
The Texas
The Texas is a historic steam locomotive famed for its role in the 1862 Great Locomotive Chase during the American Civil War.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54f02dc248190a82341eeb2473593 |
completed | April 19, 2026, 9:54 p.m. |
Created at: April 10, 2026, 11:46 a.m.