Triple
T10912889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plaça de Sant Felip Neri |
E257744
|
entity |
| Predicate | hasHistoricalDamage |
P92407
|
FINISHED |
| Object | bombing scars on church façade |
—
|
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: bombing scars on church façade | Statement: [Plaça de Sant Felip Neri, hasHistoricalDamage, bombing scars on church façade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricalDamage Context triple: [Plaça de Sant Felip Neri, hasHistoricalDamage, bombing scars on church façade]
-
A.
hasTypeOfDamage
chosen
Indicates that an entity experiences or exhibits a specific kind or category of damage.
-
B.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
C.
hasFireHistory
Indicates that an entity has experienced one or more fire events in the past.
-
D.
hasDam
Indicates that a watercourse, reservoir, or similar feature is impounded or controlled by a specific dam.
-
E.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7707310b0819092d0140acc3c64ba |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d70d3f9dc88190a686a8b0dd6a3b21 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:22 p.m.