Triple
T2724105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loretto Chapel |
E60148
|
entity |
| Predicate | hasOwnershipHistory |
P22003
|
FINISHED |
| Object | formerly owned by Sisters of Loretto |
—
|
LITERAL 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: formerly owned by Sisters of Loretto | Statement: [Loretto Chapel, hasOwnershipHistory, formerly owned by Sisters of Loretto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOwnershipHistory Context triple: [Loretto Chapel, hasOwnershipHistory, formerly owned by Sisters of Loretto]
-
A.
ownershipHistory
chosen
Indicates the sequence of past and present owners associated with an entity over time.
-
B.
hasTypeHistory
Indicates that an entity is associated with a record or sequence of its past and present types or classifications over time.
-
C.
hasTransportHistoryAs
Indicates that an entity has a record or log of being transported, characterized or classified in a specific way.
-
D.
hasPolicyHistory
Indicates that an entity is associated with a record or sequence of past policies that have applied to it over time.
-
E.
hasTransportHistory
Indicates that there exists a record or sequence of past transportation-related events or movements associated with an entity.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdacc0a6881909b64a4d22e1d7690 |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd82586f88190a98f60d3247fe2d3 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:55 p.m.