Triple
T24004090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Bridge |
E594323
|
entity |
| Predicate | nearbyStructuresDestroyed |
P1583
|
FINISHED |
| Object | buildings at both ends of the bridge |
—
|
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: buildings at both ends of the bridge | Statement: [Old Bridge, nearbyStructuresDestroyed, buildings at both ends of the bridge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyStructuresDestroyed Context triple: [Old Bridge, nearbyStructuresDestroyed, buildings at both ends of the bridge]
-
A.
buildingsDestroyed
chosen
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
B.
nearbySettlementDestroyed
Indicates that a settlement located close to a reference place has been destroyed.
-
C.
numberOfBusinessesDestroyed
Indicates the quantity of businesses that have been destroyed in a given event or context.
-
D.
secondBuildingDestroyedBy
Indicates that the second building in a specified context is the one that was destroyed by a particular agent or event.
-
E.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
- 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_69e288b9ecf08190b8c94a278f5674fe |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d4681fd88190949c5c91d4f94910 |
completed | April 29, 2026, 9:50 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:40 p.m.