Triple
T18555120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torensluis |
E453482
|
entity |
| Predicate | towerDemolishedIn |
P132139
|
FINISHED |
| Object | 1829 |
—
|
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: 1829 | Statement: [Torensluis, towerDemolishedIn, 1829]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: towerDemolishedIn Context triple: [Torensluis, towerDemolishedIn, 1829]
-
A.
towerCollapsedIn
Indicates that a tower underwent structural failure and collapsed at or within a specified location or context.
-
B.
mostStructuresDemolished
Indicates that the subject is the entity responsible for demolishing the greatest number of structures within a given context or set.
-
C.
demolishedWith
Indicates that one entity was destroyed or torn down using another specified tool, method, or agent.
-
D.
fortressDemolishedIn
Indicates that a fortress was demolished or destroyed during a specified time or event.
-
E.
townHallDemolishedIn
Indicates that a town hall building was demolished in the specified location or during the specified event or time period.
- 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53806046481908efbbe6909b2c68b |
completed | April 19, 2026, 8:16 p.m. |
| PD | Predicate disambiguation | batch_69e469e274a48190a570b25cfef4d890 |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2b93bc8190a6070018d7046547 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:38 a.m.