Triple
T4620380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hlohov |
E100962
|
entity |
| Predicate | hasRebuiltOldTown |
P23506
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Hlohov, hasRebuiltOldTown, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRebuiltOldTown Context triple: [Hlohov, hasRebuiltOldTown, true]
-
A.
hasRebuiltHistoricCenter
chosen
Indicates that an entity has undertaken and completed the restoration or reconstruction of a historic city or town center.
-
B.
cityRebuiltAfter
Indicates that a city was reconstructed or significantly restored following a prior event of destruction or severe damage.
-
C.
hasReconstructedChurch
Indicates that an entity has rebuilt or restored a church, typically after damage, destruction, or disuse.
-
D.
largelyRebuilt
Indicates that an entity has been substantially reconstructed or renovated, with most of its original structure replaced or significantly altered.
-
E.
hasNewTown
Indicates that an entity is associated with, or has established, a newly created town.
- 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_69bd43cf363c819087fd5ab441b4a3f4 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd59e560f481908abb1a97b4ff5795 |
completed | March 20, 2026, 2:29 p.m. |
| PD | Predicate disambiguation | batch_69bd5231db7c8190b38d4fdbad8bf842 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:12 p.m.