Triple
T37753431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas City disaster of 1947 |
E941044
|
entity |
| Predicate | hasBlastEffect |
P173429
|
FINISHED |
| Object | shock wave felt miles away |
—
|
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: shock wave felt miles away | Statement: [Texas City disaster of 1947, hasBlastEffect, shock wave felt miles away]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBlastEffect Context triple: [Texas City disaster of 1947, hasBlastEffect, shock wave felt miles away]
-
A.
explosionEffect
chosen
Indicates that one entity causes or is associated with an explosive event that produces a sudden, forceful effect on its surroundings.
-
B.
bustEffect
Indicates an action or event that causes something to fail, collapse, or be ruined, often abruptly or disastrously.
-
C.
warheadEffect
Indicates the type or nature of impact, damage, or outcome produced when a warhead is used or detonated.
-
D.
fireEffect
Indicates that one entity produces, causes, or is associated with a fire-related impact or consequence on another entity.
-
E.
hasFrequentExplosions
Indicates that the subject regularly experiences or produces explosions occurring at short or recurring intervals.
- 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_69f76ee1f3a88190834e6c8af99bccc9 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd166a488190b1bf9316b0790801 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:19 p.m.