Triple
T4979444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lock Haven, Pennsylvania |
E111846
|
entity |
| Predicate | experiencedMajorFlood |
P39938
|
FINISHED |
| Object | 1972 Hurricane Agnes flooding |
—
|
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: 1972 Hurricane Agnes flooding | Statement: [Lock Haven, Pennsylvania, experiencedMajorFlood, 1972 Hurricane Agnes flooding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: experiencedMajorFlood Context triple: [Lock Haven, Pennsylvania, experiencedMajorFlood, 1972 Hurricane Agnes flooding]
-
A.
floodEvent
chosen
Indicates an occurrence of a flooding event affecting a location, time period, or set of impacted entities.
-
B.
experiencedMajorTsunami
Indicates that the subject has undergone or been affected by a large-scale, significant tsunami event.
-
C.
floodConsequence
Indicates the resulting effects, outcomes, or impacts that occur as a consequence of a flood event.
-
D.
notableFloodEvents
Indicates that there are significant or historically important flood occurrences associated with the given entity.
-
E.
floodCause
Indicates that one entity is the cause or source of a flood affecting another entity or area.
- 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd7146e6e881908a55ab2756b631f6 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:33 p.m.