Triple
T37753417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas City disaster of 1947 |
E941044
|
entity |
| Predicate | hasExplosionCount |
P18304
|
FINISHED |
| Object | multiple explosions |
—
|
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: multiple explosions | Statement: [Texas City disaster of 1947, hasExplosionCount, multiple explosions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExplosionCount Context triple: [Texas City disaster of 1947, hasExplosionCount, multiple explosions]
-
A.
numberOfExplosions
chosen
Indicates the count of distinct explosion events associated with an entity or situation.
-
B.
hasFrequentExplosions
Indicates that the subject regularly experiences or produces explosions occurring at short or recurring intervals.
-
C.
numberOfFailedBombs
Indicates the count of bombs associated with an entity that did not successfully detonate or function as intended.
-
D.
explosionOccurred
Indicates that an explosion event has taken place at a specific time and/or location.
-
E.
hasBurstDamagePotential
Indicates the extent to which an entity can inflict a large amount of damage in a short, concentrated period of time.
- 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_69fd67191cf88190b53ecbf5be3564e9 |
completed | May 8, 2026, 4:31 a.m. |
| PD | Predicate disambiguation | batch_69fd654fdaac81908e67e75194710f06 |
completed | May 8, 2026, 4:23 a.m. |
Created at: May 3, 2026, 4:19 p.m.