Triple
T24514571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish City |
E606327
|
entity |
| Predicate | hasUseAfterRestoration |
P156574
|
FINISHED |
| Object | restaurants |
—
|
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: restaurants | Statement: [Spanish City, hasUseAfterRestoration, restaurants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUseAfterRestoration Context triple: [Spanish City, hasUseAfterRestoration, restaurants]
-
A.
hasBeenRestored
Indicates that an entity has been returned to a previous or original state after damage, loss, or alteration.
-
B.
hasRestoration
Indicates that an entity has undergone, is undergoing, or is associated with a process of repair, renewal, or restoration.
-
C.
hasRestorationFocus
Indicates that something is primarily concerned with or directed toward restoration or repair.
-
D.
afterRestoration
Indicates that one state, condition, or event occurs subsequent to the completion of a restoration process on an entity.
-
E.
hasRestorationApproach
Indicates the method or strategy used to restore or rehabilitate something to a desired state.
- 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_69e2c4c725148190a4e41577c5cb409c |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2a9d795288190916368e3cec1f666 |
completed | April 30, 2026, 1:01 a.m. |
Created at: April 18, 2026, 2:24 a.m.