Triple
T4153975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2010 Chile earthquake |
E89971
|
entity |
| Predicate | infrastructureDamage |
P54661
|
FINISHED |
| Object | roads and bridges damaged |
—
|
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: roads and bridges damaged | Statement: [2010 Chile earthquake, infrastructureDamage, roads and bridges damaged]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: infrastructureDamage Context triple: [2010 Chile earthquake, infrastructureDamage, roads and bridges damaged]
-
A.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
B.
economicDamageApprox
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
-
C.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
D.
numberOfDistrictsHeavilyDamaged
Indicates the count of districts that have sustained severe or heavy damage in a given context or event.
-
E.
isMajorDamOf
Indicates that one dam is the primary or most significant dam associated with a particular river, reservoir, or water system.
- 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_69aed95a59a881909b26e70b42c6811a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af033ef6648190adde17f943d89c78 |
completed | March 9, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69af018c101081909070da5b11e5eb3d |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af033d94888190b34349e355b874ef |
completed | March 9, 2026, 5:28 p.m. |
Created at: March 9, 2026, 3:44 p.m.