Triple
T1241017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beirut port explosion of 4 August 2020 |
E26656
|
entity |
| Predicate | economicDamageApprox |
P25888
|
FINISHED |
| Object | between 3 and 15 billion US dollars |
—
|
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: between 3 and 15 billion US dollars | Statement: [Beirut port explosion of 4 August 2020, economicDamageApprox, between 3 and 15 billion US dollars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicDamageApprox Context triple: [Beirut port explosion of 4 August 2020, economicDamageApprox, between 3 and 15 billion US dollars]
-
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.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
-
C.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
-
D.
warDamage
Indicates damage that was caused as a direct consequence of war or armed conflict.
-
E.
buildingsDestroyed
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
- 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_69a4948689d08190b3a4a3f388c02148 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf4343e48190a232abd8475880a0 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb696a38819095845c84f0241287 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bce611ec819092cb13d354d0903e |
completed | March 1, 2026, 10:25 p.m. |
Created at: March 1, 2026, 7:47 p.m.