Triple
T8045300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Garland Transit Center |
E187532
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Garland |
E9807
|
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: Garland | Statement: [South Garland Transit Center, city, Garland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garland Context triple: [South Garland Transit Center, city, Garland]
-
A.
Garland
chosen
Garland is a large suburban city in the Dallas–Fort Worth metropolitan area known for its diverse community and mixed residential, commercial, and industrial character.
-
B.
Garland
Garland is a faint dwarf galaxy that is a member of the nearby M81 Group of galaxies.
-
C.
Garland Greene
Garland Greene is a notorious, eerily soft-spoken serial killer character from the action film "Con Air," portrayed by Steve Buscemi.
-
D.
Doc Boone
Doc Boone is the hard-drinking yet compassionate frontier doctor character from John Ford’s classic Western film "Stagecoach."
-
E.
LeRoy
LeRoy is the middle name of American political consultant and Republican strategist Lee Atwater.
- 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f4c79388190aecee6e313071a17 |
completed | March 31, 2026, 3:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc5711d2bc8190911f2cade7596be5 |
completed | March 31, 2026, 11:21 p.m. |
Created at: March 30, 2026, 5:24 p.m.