Triple
T36315869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Sant Felip Neri |
E894188
|
entity |
| Predicate | nearbyFacadeDamage |
P87330
|
FINISHED |
| Object | bomb shrapnel marks on surrounding walls |
—
|
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: bomb shrapnel marks on surrounding walls | Statement: [School of Sant Felip Neri, nearbyFacadeDamage, bomb shrapnel marks on surrounding walls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyFacadeDamage Context triple: [School of Sant Felip Neri, nearbyFacadeDamage, bomb shrapnel marks on surrounding walls]
-
A.
nearestDam
Indicates that one dam is the closest in distance to a given reference location or entity compared to all other dams.
-
B.
notableLocationOfDamage
chosen
Indicates the specific place where damage is prominently present or has significantly occurred.
-
C.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
D.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
E.
hasNearbyInfrastructureType
Indicates that an entity is located close to infrastructure of a specified type.
- 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_69f76e4d1a788190a6ab6ccca28547a7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fee0b2da3c8190a3519d0564f2f32d |
completed | May 9, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69fee05b315c819081dfcbfb15273487 |
completed | May 9, 2026, 7:20 a.m. |
Created at: May 3, 2026, 4:09 p.m.