Triple
T24530697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrick Wayne Lorenz |
E606798
|
entity |
| Predicate | hasFacilityNamedAfter |
P26964
|
FINISHED |
| Object |
Lorenz Field
Lorenz Field is an athletic facility named in honor of Patrick Wayne Lorenz, likely serving as a venue for sports events and community activities.
|
E149028
|
NE FINISHED |
How this triple was built (3 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: Lorenz Field | Statement: [Patrick Wayne Lorenz, hasFacilityNamedAfter, Lorenz Field]
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: Lorenz Field Triple: [Patrick Wayne Lorenz, hasFacilityNamedAfter, Lorenz Field]
Generated description
Lorenz Field is an athletic facility named in honor of Patrick Wayne Lorenz, likely serving as a venue for sports events and community activities.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacilityNamedAfter Context triple: [Patrick Wayne Lorenz, hasFacilityNamedAfter, Lorenz Field]
-
A.
hasPlaceNamedAfter
Indicates that one place is named in honor of or derived from the name of another place.
-
B.
isNamedFacilityOf
chosen
Indicates that a facility bears the official name associated with a particular entity (such as an organization, person, or place).
-
C.
hasParkNamedAfter
Indicates that one entity has a park that is named in honor of, or after, another entity.
-
D.
hasCapitalNamedAfter
Indicates that one entity serves as the capital of another entity and that this capital is named after the other entity.
-
E.
hasHeritageSiteNamedAfter
Indicates that one entity has a heritage site that is named after another entity.
- F. None of above.
Provenance (6 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_69e2c4c90c848190b23c4303620dcaaf |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fee9dee788190bb854894e3be8845 |
completed | May 22, 2026, 5:50 a.m. |
| NEDg | Description generation | batch_6a0fefb19fa881909157ec86c395b682 |
completed | May 22, 2026, 5:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ff0cc90508190b5d68bedeb4531aa |
completed | May 22, 2026, 5:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b0ca8081908d931aec560eae56 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:25 a.m.