Triple
T37005463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frances Carrick Thomas |
E915779
|
entity |
| Predicate | hasNameOnBuilding |
P147423
|
FINISHED |
| Object | J. Douglas Gay Jr. / Frances Carrick Thomas Library |
—
|
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: J. Douglas Gay Jr. / Frances Carrick Thomas Library | Statement: [Frances Carrick Thomas, hasNameOnBuilding, J. Douglas Gay Jr. / Frances Carrick Thomas Library]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNameOnBuilding Context triple: [Frances Carrick Thomas, hasNameOnBuilding, J. Douglas Gay Jr. / Frances Carrick Thomas Library]
-
A.
containsBuilding
Indicates that one location or area includes a building within its boundaries.
-
B.
mainBuildingName
Indicates the name that is designated as the primary or main building associated with an entity.
-
C.
refersToBuildingOn
chosen
Indicates that one entity explicitly references or designates a specific building as its subject or target.
-
D.
hasBuildingLocation
Indicates that a building is situated at, or associated with, a specific geographic or spatial location.
-
E.
isFourthBuildingToBearName
Indicates that the subject is the fourth building to carry or be designated with a particular name.
- F. None of above.
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_69f76e90ed548190b187d2475f5c807d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff8cecbf048190860b9f72b8753f5c |
completed | May 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69ff8c4c39dc8190b5bf35adc1bae7c6 |
completed | May 9, 2026, 7:34 p.m. |
Created at: May 3, 2026, 4:14 p.m.