Triple
T8621343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aqua Appia |
E204170
|
entity |
| Predicate | builtUnderOfficeOf |
P84482
|
FINISHED |
| Object | censor |
—
|
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: censor | Statement: [Aqua Appia, builtUnderOfficeOf, censor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: builtUnderOfficeOf Context triple: [Aqua Appia, builtUnderOfficeOf, censor]
-
A.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
B.
officeUnder
Indicates that one office is subordinate to, managed by, or organizationally within the authority of another office.
-
C.
establishedOffice
Indicates that an entity created or set up an official office or place of operation.
-
D.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
E.
representsInOffice
Indicates that one entity serves as an official representative of another entity within a specific office, position, or institutional role.
- F. None of above. chosen
Provenance (4 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc5730309081909a9a0256c9bf5f8f |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc455906f8819082edd79cb4a1cf28 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc572d99bc819097f36b140c2ee1ce |
completed | March 31, 2026, 11:22 p.m. |
Created at: March 30, 2026, 6:26 p.m.