Triple
T26395173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leaning Tower of Pisa |
E663526
|
entity |
| Predicate | stabilizationWork |
P160499
|
FINISHED |
| Object | late 20th century |
—
|
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: late 20th century | Statement: [Leaning Tower of Pisa, stabilizationWork, late 20th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stabilizationWork Context triple: [Leaning Tower of Pisa, stabilizationWork, late 20th century]
-
A.
stabilizationOutcome
Indicates the result or state achieved after a stabilization process has been applied to something.
-
B.
stabilizedBy
Indicates that an entity’s state, structure, or behavior is made more steady, secure, or resistant to change through the influence or support of another entity.
-
C.
stabilizationCompensation
Indicates a compensatory action or mechanism that counteracts changes or disturbances in order to maintain or restore stability in a system or process.
-
D.
reconstructionWork
Indicates that an entity is engaged in or associated with activities to rebuild, restore, or repair something that was damaged, destroyed, or altered.
-
E.
reconstructionWorkBy
Indicates that a reconstruction project or effort is carried out, overseen, or authored by a specific agent or entity.
- 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_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f610c2e40c8190b6b1314966e52706 |
completed | May 2, 2026, 2:57 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f6018ceb1c8190a6a5f84071659a96 |
completed | May 2, 2026, 1:52 p.m. |
Created at: April 26, 2026, 11:28 p.m.