Triple
T5604229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AIOWF |
E147192
|
entity |
| Predicate | acronym |
P43
|
FINISHED |
| Object | AIOWF |
E147192
|
NE 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: AIOWF | Statement: [AIOWF, acronym, AIOWF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AIOWF Context triple: [AIOWF, acronym, AIOWF]
-
A.
AIOWF
chosen
AIOWF is the collective body representing the international federations that govern sports featured in the Olympic Winter Games.
-
B.
OWF
OWF is the acronym for the Office of Wildland Fire, a U.S. federal office responsible for coordinating and overseeing wildland fire management policies and programs.
-
C.
AWS WAF
AWS WAF is a cloud-based web application firewall service that helps protect web applications and APIs from common web exploits and bots.
-
D.
ACWF
ACWF is the All-China Women's Federation, a mass organization in China dedicated to promoting women's rights, interests, and gender equality.
-
E.
BeEF
BeEF (Browser Exploitation Framework) is a penetration testing tool focused on exploiting web browsers to assess and demonstrate client-side security vulnerabilities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c0090500f881908374285baf0ac46f |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020f9408481908cf006074c726301 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0287649cc8190ae356790dd993973 |
completed | March 22, 2026, 5:35 p.m. |
Created at: March 22, 2026, 3:39 p.m.