Triple
T5331878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Mosque of Kairouan |
E123327
|
entity |
| Predicate | hasLibraryHistorically |
P62873
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Great Mosque of Kairouan, hasLibraryHistorically, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLibraryHistorically Context triple: [Great Mosque of Kairouan, hasLibraryHistorically, yes]
-
A.
hasHistoricalSection
Indicates that something includes a dedicated part or segment that presents historical information or context.
-
B.
hasHistoricalText
Indicates that an entity is associated with a historical written document or record describing it or its past.
-
C.
hasHistoricalEntity
Indicates a relationship where one entity includes, references, or is associated with another entity that existed or is defined in a past historical context.
-
D.
hasLegacy
Indicates that an entity leaves behind a lasting impact, influence, or inheritance that continues to exist or be recognized over time.
-
E.
hasHistoricalOrigin
Indicates that something originated, was first established, or came into existence during a specific historical period or context.
- 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_69bd46477f9081909d242a327d749466 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85aab0308190990626cbc9da3e21 |
completed | March 20, 2026, 5:36 p.m. |
| PD | Predicate disambiguation | batch_69bd84583dbc819088a03e3afb30178c |
completed | March 20, 2026, 5:31 p.m. |
| PDg | Predicate description generation | batch_69bd8501d53c81908371bd5195ba5703 |
completed | March 20, 2026, 5:33 p.m. |
Created at: March 20, 2026, 2 p.m.