Triple
T17881644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William P. Hobby Airport |
E447098
|
entity |
| Predicate | FAA LID |
P420
|
FINISHED |
| Object | HOU |
—
|
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: HOU | Statement: [William P. Hobby Airport, FAA LID, HOU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HOU Context triple: [William P. Hobby Airport, FAA LID, HOU]
-
A.
HOU
HOU is the commonly used acronym for the Hellenic Open University, a Greek institution specializing in distance and lifelong learning.
-
B.
Houston
Houston is a village in Renfrewshire, Scotland, known for its historic conservation area and role as a commuter settlement near Glasgow.
-
C.
Houston
chosen
Houston is a major U.S. metropolis known for its energy industry, NASA’s Johnson Space Center, and its diverse, rapidly growing population.
-
D.
Hou
Hou is a Chinese surname and given name that can represent various historical figures, modern individuals, and fictional characters depending on context.
-
E.
Huston
Huston is a surname most famously associated with a prominent American film family that includes acclaimed director John Huston and actress Anjelica Huston.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c0f40fc8190b2e615b41829f5df |
completed | April 19, 2026, 9:10 a.m. |
Created at: April 10, 2026, 10:18 a.m.