Triple
T872740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black September conflict |
E18848
|
entity |
| Predicate | approximateNumberOfCasualties |
P661
|
FINISHED |
| Object | thousands of people killed and wounded |
—
|
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: thousands of people killed and wounded | Statement: [Black September conflict, approximateNumberOfCasualties, thousands of people killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfCasualties Context triple: [Black September conflict, approximateNumberOfCasualties, thousands of people killed and wounded]
-
A.
casualtiesEstimate
chosen
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
B.
militaryCasualtiesEstimate
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
C.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
casualtiesDescription
Indicates a textual description of the human losses (such as deaths, injuries, or missing persons) resulting from an event or incident.
- 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac97d0f88190b67fcb7fc058e4b9 |
completed | March 1, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8b9b5c81909ac71904f8b8b5cd |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.