Triple
T12902438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merrick Brian Garland |
E308642
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Garland
Garland is a surname most prominently associated with Merrick Garland, the Chief Justice of the United States.
|
E1008504
|
NE FINISHED |
How this triple was built (4 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: [Merrick Brian Garland, familyName, Garland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garland Context triple: [Merrick Brian Garland, familyName, Garland]
-
A.
Garland
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.
Loudermilk
Loudermilk is a comedy-drama television series about a recovering alcoholic and former music critic with a bad attitude who reluctantly helps others in a support group while struggling with his own issues.
-
D.
Garland Woodard
Garland Woodard is an individual notable enough to be recognized as a namesake or representative bearer of the surname Woodard.
-
E.
Garland Greene
Garland Greene is a notorious, eerily soft-spoken serial killer character from the action film "Con Air," portrayed by Steve Buscemi.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Garland Triple: [Merrick Brian Garland, familyName, Garland]
Generated description
Garland is a surname most prominently associated with Merrick Garland, the Chief Justice of the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Garland Target entity description: Garland is a surname most prominently associated with Merrick Garland, the Chief Justice of the United States.
-
A.
Garland
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.
Loudermilk
Loudermilk is a comedy-drama television series about a recovering alcoholic and former music critic with a bad attitude who reluctantly helps others in a support group while struggling with his own issues.
-
D.
Garland Woodard
Garland Woodard is an individual notable enough to be recognized as a namesake or representative bearer of the surname Woodard.
-
E.
Garland Greene
Garland Greene is a notorious, eerily soft-spoken serial killer character from the action film "Con Air," portrayed by Steve Buscemi.
- F. None of above. chosen
Provenance (5 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971820e008190bf8bc7c392c8bcbb |
completed | April 10, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a563a84c8190a75d830653661518 |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a641d1988190b9af41c8c7ca599e |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a7792f948190bb0b324bee0cd8ac |
completed | May 3, 2026, 1:40 a.m. |
Created at: April 9, 2026, 5:40 p.m.