Triple
T3824372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Pavilion |
E88649
|
entity |
| Predicate | fireEffect |
P52514
|
FINISHED |
| Object | acrylic skin destroyed |
—
|
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: acrylic skin destroyed | Statement: [United States Pavilion, fireEffect, acrylic skin destroyed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fireEffect Context triple: [United States Pavilion, fireEffect, acrylic skin destroyed]
-
A.
fires
Indicates that an agent initiates the discharge or ignition of something, such as a weapon, engine, or explosive device, causing it to operate or go off.
-
B.
fireAdaptation
Indicates that an entity possesses traits or mechanisms that enable it to survive, reproduce, or otherwise benefit in environments where fire occurs.
-
C.
fireModes
Indicates the different ways or settings in which a weapon or device can be fired or operated.
-
D.
notableFire
Indicates that a significant or historically important fire event is associated with the subject.
-
E.
warheadEffect
Indicates the type or nature of impact, damage, or outcome produced when a warhead is used or detonated.
- 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_69aed9538cf881909d9ce8ca4ac7c18c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef188b474819087680db42b04ecdd |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee74a2bc081909b237df8b1e27653 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef18748648190b85e62f7796ff4b4 |
completed | March 9, 2026, 4:12 p.m. |
Created at: March 9, 2026, 3:17 p.m.